Udacity - Self-Driving Car Engineer nd013 v1.0.0

mp4   Hot：401   Size：7.17 GB   Created：2019-04-15 09:54:15   Update：2021-12-13 06:16:53  

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File List

  • Part 01-Module 01-Lesson 01_Welcome/12. The Great Robot Race-saVZ_X9GfIM.mp4 316.54 MB
    Part 03-Module 02-Lesson 05_Path Planning Project/06. Path Planning Project Walkthrough and QA-7sI3VHFPP0w.mp4 280.92 MB
    Part 06-Module 01-Lesson 08_Technical Interviewing Techniques/07. Coding-zhQYREUI8Z0.mp4 105.02 MB
    Part 03-Module 04-Lesson 08_Elective Project Functional Safety/02. Functional Safety Walkthrough-SsXNj_pfnao.mp4 89.96 MB
    Part 03-Module 03-Lesson 05_Semantic Segmentation Project/02. ADL Project Walkthrough V2-5g9sZIwGubk.mp4 88.15 MB
    Part 03-Module 02-Lesson 04_Trajectory Generation/06. A - Artificial Intelligence for Robotics-lxCCtgHk5Vs.mp4 76.44 MB
    Part 03-Module 02-Lesson 01_Search/14. A-lxCCtgHk5Vs.mp4 76.44 MB
    Part 03-Module 02-Lesson 01_Search/11. First Search Program - Artificial Intelligence for Robotics-TPIFP4E7DVo.mp4 61.35 MB
    Part 02-Module 03-Lesson 05_Particle Filters/14. Importance Weight-xP9PrSTJPz0.mp4 49.02 MB
    Part 03-Module 02-Lesson 01_Search/11. First Search Program Solution - Artificial Intelligence for Robotics-cl8Kdkr4Gbg.mp4 46.35 MB
    Part 03-Module 05-Lesson 05_System Integration Project/06. SDC Capstone Portfolio Part 1 V1 V2-6GIFyUzhaQo.mp4 44.86 MB
    Part 01-Module 02-Lesson 01_Career Services Available to You/04. Working at Uber ATG-V23NZzX0efY.mp4 40.53 MB
    Part 02-Module 03-Lesson 02_Localization Overview/03. Total Probability-n1EacrqyCs8.mp4 40.4 MB
    Part 01-Module 02-Lesson 01_Career Services Available to You/03. Working at NVIDIA-C6Rt9lxMqHs.mp4 39.55 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/25. Kalman Matrices-LEuzK9X7_hM.mp4 35.56 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/23. More Kalman Filters-hUnTg5v4tDU.mp4 35.46 MB
    Part 03-Module 02-Lesson 01_Search/15. Implement A-SSyvcCZKlqo.mp4 34.87 MB
    Part 03-Module 02-Lesson 01_Search/22. Left Turn Policy-bQA2ELDNmmg.mp4 34.34 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/18. Kalman Filter Code-X7cixvcogl8.mp4 33.73 MB
    Part 02-Module 03-Lesson 01_Introduction to Localization/01. L10 Localization Overview A01 Intro To AI-8p4C7jvfwvQ.mp4 32.73 MB
    Part 05-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4 32.54 MB
    Part 03-Module 02-Lesson 01_Search/16. A in Action-qXZt-B7iUyw.mp4 32.21 MB
    Part 02-Module 03-Lesson 02_Localization Overview/01. Introduction-Uqt_pRbR8rI.mp4 32 MB
    Part 06-Module 01-Lesson 08_Technical Interviewing Techniques/05. Brainstorming-LJFYhMDCCsU.mp4 31.66 MB
    Part 03-Module 02-Lesson 05_Path Planning Project/img/circle-path.gif 31.35 MB
    Part 03-Module 05-Lesson 05_System Integration Project/12. SDC Capstone Portfolio Part 4 V1-2tDrj8KjIL4.mp4 31.31 MB
    Part 06-Module 01-Lesson 08_Technical Interviewing Techniques/08. Debugging-Bz1tlvkql9Q.mp4 31.04 MB
    Part 03-Module 02-Lesson 05_Path Planning Project/img/car-in-line.gif 29.98 MB
    Part 01-Module 02-Lesson 01_Career Services Available to You/02. Working at Mercedes-Benz-Z_hi4djW5aw.mp4 29.96 MB
    Part 03-Module 02-Lesson 01_Search/22. Left Turn Policy-rH5DKpwYQLY.mp4 29.76 MB
    Part 01-Module 01-Lesson 01_Welcome/04. What Projects Will You Build-JGpXenoW0dk.mp4 29.36 MB
    Part 02-Module 03-Lesson 05_Particle Filters/06. Particle Filters-4S-sx5_cmLU.mp4 29.09 MB
    Part 02-Module 03-Lesson 02_Localization Overview/32. Bayes' Rule-sA5wv56qYc0.mp4 29 MB
    Part 02-Module 04-Lesson 01_PID Control/13. Twiddle - Artificial Intelligence for Robotics-2uQ2BSzDvXs.mp4 28.71 MB
    Part 03-Module 05-Lesson 05_System Integration Project/10. SDC Capstone Portfolio Part 3 V1 V1-oTfArPhstQU.mp4 28.52 MB
    Part 03-Module 02-Lesson 01_Search/17. Dynamic Programming-r2bPY2s9wII.mp4 28.3 MB
    Part 02-Module 03-Lesson 05_Particle Filters/20. Resampling Wheel-wNQVo6uOgYA.mp4 27.89 MB
    Part 03-Module 06-Lesson 01_Completing the Program/01. CarND Final Video-V2-hDsYM2Zd0jg.mp4 27.86 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/24. Kalman Filter Design-KYEr4BXhD_E.mp4 27.34 MB
    Part 03-Module 05-Lesson 05_System Integration Project/08. SDC Capstone Portfolio Part 2 V2-kdfXo6atphY.mp4 26.83 MB
    Part 02-Module 03-Lesson 05_Particle Filters/25. Filters-bjZy-RVms_8.mp4 25.34 MB
    Part 03-Module 02-Lesson 04_Trajectory Generation/08. Hybrid A Introduction-NuurQejBk0o.mp4 25.19 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/02. ML in The Google Self-Driving Car-lL16AQItG1g.mp4 25.09 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4 25.04 MB
    Part 06-Module 01-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4 24.68 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/16. Gaussian Motion-X7YggdDnLaw.mp4 24.56 MB
    Part 02-Module 03-Lesson 05_Particle Filters/20. Resampling Wheel-aHLslaWO-AQ.mp4 23.62 MB
    Part 02-Module 04-Lesson 01_PID Control/07. PD Controller - Artificial Intelligence for Robotics-kVYy2kjZjhA.mp4 23.27 MB
    Part 03-Module 02-Lesson 01_Search/20. Value Program-FdT1g_Bzjm0.mp4 23.26 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/20. Kalman Filter Land-LXJ5jrvDuEk.mp4 22.64 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4 21.79 MB
    Part 06-Module 01-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4 20.72 MB
    Part 05-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4 20.63 MB
    Part 02-Module 03-Lesson 05_Particle Filters/15. Resampling-zlCJQmxvrkE.mp4 20.61 MB
    Part 02-Module 03-Lesson 05_Particle Filters/01. Field Trip-2ocy_7PJtfA.mp4 20.55 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/14. Gaussian NB Example-wpnDwiqTCJA.mp4 20.28 MB
    Part 06-Module 01-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4 19.91 MB
    Part 02-Module 04-Lesson 01_PID Control/14. Parameter Optimization - Artificial Intelligence for Robotics-A2b3F5Ae53Y.mp4 19.54 MB
    Part 01-Module 04-Lesson 07_Decision Trees/33. Bias-Variance Dilemma-W5uUYnSHDhM.mp4 19.11 MB
    Part 02-Module 04-Lesson 05_Model Predictive Control Project/img/mpc-vid2.gif 18.87 MB
    Part 02-Module 03-Lesson 05_Particle Filters/07. Using Robot Class-1hgVZtRIjFU.mp4 18.8 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/25. Kalman Matrices-ade97UKqSIc.mp4 18.76 MB
    Part 06-Module 01-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.mp4 18.44 MB
    Part 02-Module 04-Lesson 01_PID Control/04. Implement P Controller - Artificial Intelligence for Robotics-OrJgrTc5d04.mp4 18.25 MB
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.mp4 18.22 MB
    Part 03-Module 02-Lesson 01_Search/04. Motion Planning-KHAu5A_flcQ.mp4 18.17 MB
    Part 03-Module 02-Lesson 01_Search/23. Planning Conclusion-M7ZJ74RVHqo.mp4 18.06 MB
    Part 02-Module 04-Lesson 06_The End/01. CarND Term 2 Outro-dLOe-KiTEw8.mp4 17.87 MB
    Part 02-Module 03-Lesson 02_Localization Overview/02. Localization-31xZhj2uPr4.mp4 17.79 MB
    Part 02-Module 03-Lesson 02_Localization Overview/34. Theorem of Total Probability-byZ-BzbQA5M.mp4 17.79 MB
    Part 02-Module 03-Lesson 02_Localization Overview/27. Sense and Move 2-rmWL_3r8MKo.mp4 17.54 MB
    Part 05-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.mp4 17.37 MB
    Part 01-Module 04-Lesson 08_Object Detection/02. Intro to Arpan and Drew-zfWvntpxbK0.mp4 17.32 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/11. Parameter Update-d8UrbKKlGxI.mp4 17.31 MB
    Part 02-Module 03-Lesson 02_Localization Overview/23. Limit Distribution Quiz-SXSafquSoW8.mp4 17.04 MB
    Part 02-Module 04-Lesson 01_PID Control/15. Parameter Optimization Solution - Artificial Intelligence for Robotics-YQ5Pa-OKQm0.mp4 17.04 MB
    Part 02-Module 02-Lesson 01_Introduction and Sensors/03. Radar Strengths And Weaknesses-m7kpRg3bEI8.mp4 16.98 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/19. Playing Around with Kernel Choices-krV6r7HxmZU.mp4 16.93 MB
    Part 02-Module 03-Lesson 05_Particle Filters/23. Error-UAdcKWLi9G8.mp4 16.76 MB
    Part 06-Module 01-Lesson 08_Technical Interviewing Techniques/02. Clarifying the Question-XvvKBmKC_84.mp4 16.72 MB
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4 16.67 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/20. GoogLeNet-sdT5f8n7IcI.mp4 16.58 MB
    Part 05-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.mp4 16.57 MB
    Part 06-Module 01-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4 16.53 MB
    Part 01-Module 01-Lesson 01_Welcome/01. Welcome-jHA__A61nqc.mp4 16.21 MB
    Part 02-Module 02-Lesson 01_Introduction and Sensors/02. Introduction-4E6RtK_Ml1I.mp4 16.18 MB
    Part 01-Module 04-Lesson 08_Object Detection/40. Traditional vs. Deep Learning Approach-_IFdaC0lWhI.mp4 16.14 MB
    Part 01-Module 04-Lesson 07_Decision Trees/10. Decision Tree Parameters-jkJ4dbbpVCQ.mp4 15.95 MB
    Part 03-Module 05-Lesson 05_System Integration Project/img/rosbag-play.gif 15.84 MB
    Part 03-Module 04-Lesson 02_Introduction to Functional Safety/06. L1 Sebastian-lw3O5Me6FRw.mp4 15.79 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/22. SVM Gamma Parameter-m2a2K4lprQw.mp4 15.5 MB
    Part 02-Module 03-Lesson 02_Localization Overview/26. Sense and Move-K8g3Hss8Q1A.mp4 15.44 MB
    Part 01-Module 04-Lesson 10_The End/01. CarND Term 1 Outro-5Apiqg20W8M.mp4 15.42 MB
    Part 03-Module 01-Lesson 01_Welcome/01. CarND Term 3 Intro-v4pRPMIudM0.mp4 15.27 MB
    Part 06-Module 01-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.mp4 15.22 MB
    Part 01-Module 04-Lesson 01_Camera Calibration/16. Transforming a Stop Sign-OXILkkXXY8A.mp4 15.21 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/18. Naive Bayes Strengths and Weaknesses-nfbKTrufPOs.mp4 15.2 MB
    Part 02-Module 03-Lesson 02_Localization Overview/19. Inexact Motion 1-C3f-T9_GTpw.mp4 15.07 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/02. Module Introduction-uyLRFMI4HkA.mp4 15 MB
    Part 02-Module 04-Lesson 01_PID Control/11. PID Implementation - Artificial Intelligence for Robotics-Ag8H3Iit9j4.mp4 14.91 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/19. Empirics-VT8RENbE9Ck.mp4 14.79 MB
    Part 06-Module 01-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.mp4 14.72 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/09. Shifting the Mean-8c479K2UCZo.mp4 14.6 MB
    Part 06-Module 01-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.mp4 14.59 MB
    Part 06-Module 01-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.mp4 14.4 MB
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.mp4 14.38 MB
    Part 06-Module 01-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.mp4 14.28 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/21. Kalman Filter Prediciton-HTL5-0DDqE4.mp4 14.2 MB
    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-UFcTLCttNRI.mp4 14.05 MB
    Part 01-Module 03-Lesson 10_Behavioral Cloning Project/02. Behavioral Cloning Project-YXs-IwG9ISg.mp4 13.92 MB
    Part 01-Module 03-Lesson 08_Keras/03. Deep Learning Frameworks-i2mmnu-t8-c.mp4 13.79 MB
    Part 04-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.mp4 13.74 MB
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/12. L6 Sebastian-AD47B4rAKyY.mp4 13.74 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/05. Deep Learning History-AWWLT4QxKaM.mp4 13.33 MB
    Part 01-Module 04-Lesson 07_Decision Trees/34. DT Strengths and Weaknesses-KGnhg76iRfI.mp4 13.29 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/18. Kalman Filter Code-3xBycKfnCOQ.mp4 13.28 MB
    Part 04-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.24 MB
    Part 04-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.24 MB
    Part 04-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.24 MB
    Part 03-Module 02-Lesson 01_Search/12. Expansion Grid - Artificial Intelligence for Robotics-1l7bWfz8sJw.mp4 13.18 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.17 MB
    Part 02-Module 03-Lesson 02_Localization Overview/12. Sense Function-eIjyrQpDogg.mp4 12.91 MB
    Part 04-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.85 MB
    Part 04-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.85 MB
    Part 04-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.85 MB
    Part 04-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.64 MB
    Part 04-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.64 MB
    Part 04-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.64 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/02. Bryan Catanzaro-CLIF_6QwlFo.mp4 12.55 MB
    Part 02-Module 03-Lesson 06_Implementation of a Particle Filter/21. Explanation of Project Code-3VRp4chnPE4.mp4 12.52 MB
    Part 01-Module 01-Lesson 01_Welcome/03. Overview-RZ5iolr4RGs.mp4 12.37 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/06. Imagenet-pcNxBs7OAzA.mp4 12.36 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/19. Kalman Prediction-doyrdLJ6rJ4.mp4 12.36 MB
    Part 06-Module 01-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.mp4 12.24 MB
    Part 01-Module 04-Lesson 07_Decision Trees/13. Data Impurity and Entropy-Bd15qhUrKCI.mp4 12.19 MB
    Part 04-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.mp4 12.18 MB
    Part 04-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.mp4 12.18 MB
    Part 04-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.mp4 12.18 MB
    Part 04-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.mp4 12.08 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/17. Training and Testing Data-x2dmBUEKQIA.mp4 12.06 MB
    Part 02-Module 03-Lesson 05_Particle Filters/23. Error-3kOrzhYCXz8.mp4 12.03 MB
    Part 06-Module 01-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.mp4 11.97 MB
    Part 02-Module 03-Lesson 02_Localization Overview/21. Inexact Motion 3-7T1Rr7KLgdM.mp4 11.85 MB
    Part 06-Module 01-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.mp4 11.85 MB
    Part 02-Module 01-Lesson 01_Welcome/01. Term 2 Intro-LD5VEaq1WdY.mp4 11.77 MB
    Part 01-Module 04-Lesson 08_Object Detection/39. Summary of Vehicle Detection and Tracking-3ceKmVDQfFQ.mp4 11.73 MB
    Part 03-Module 05-Lesson 02_Introduction to ROS/04. Build Robots with ROS-7eaz0qW7y_I.mp4 11.63 MB
    Part 04-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.53 MB
    Part 04-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.53 MB
    Part 04-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.53 MB
    Part 01-Module 04-Lesson 01_Camera Calibration/02. L21 Advanced Techniques For Lane Finding A02 L Welcome To Computer Vision-FRBWnuf1OIg.mp4 11.21 MB
    Part 03-Module 02-Lesson 01_Search/20. Value Program-RXpuBRA-cpo.mp4 11.15 MB
    Part 02-Module 03-Lesson 02_Localization Overview/36. Two Coin Quiz-_AhoOd8YUK0.mp4 11.03 MB
    Part 02-Module 03-Lesson 02_Localization Overview/18. Move Function-TnFq6hufsYs.mp4 10.96 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/32. What to Expect from the Project-WAt_g6HgYvs.mp4 10.83 MB
    Part 01-Module 03-Lesson 10_Behavioral Cloning Project/05. 02 - Data Collection-kTJiHXJe_t4.mp4 10.82 MB
    Part 03-Module 04-Lesson 05_Functional Safety Functional Safety Concept/11. L4 Sebastian-0L2DG60xWy8.mp4 10.72 MB
    Part 02-Module 04-Lesson 01_PID Control/02. PID Control - Artificial Intelligence for Robotics--8w0prceask.mp4 10.63 MB
    Part 01-Module 04-Lesson 01_Camera Calibration/03. Overview-yN7u0qmJDhA.mp4 10.6 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/01. Introduction-mMT_3k1LvNU.mp4 10.48 MB
    Part 01-Module 03-Lesson 10_Behavioral Cloning Project/09. 05 - Validating The Model-1UGOJGg-0dU.mp4 10.47 MB
    Part 04-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.mp4 10.45 MB
    Part 03-Module 05-Lesson 05_System Integration Project/03. Traffic Lights 2-PzIRniXv0z0.mp4 10.42 MB
    Part 01-Module 04-Lesson 01_Camera Calibration/04. Getting Started, Camera Calibration-0zlyx5nL8Uo.mp4 10.4 MB
    Part 01-Module 03-Lesson 06_LeNet for Traffic Signs/10. LeNet for Traffic Signs-VfJvSF087SI.mp4 10.32 MB
    Part 02-Module 03-Lesson 05_Particle Filters/26. 2012-QgOUu2sUDzg.mp4 10.31 MB
    Part 06-Module 01-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.mp4 10.22 MB
    Part 03-Module 02-Lesson 01_Search/21. Optimum Policy-7kllZxX-Nso.mp4 10.18 MB
    Part 03-Module 02-Lesson 01_Search/10. Maze 2-YwAyqkznxa0.mp4 10.14 MB
    Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.mp4 10.12 MB
    Part 01-Module 01-Lesson 01_Welcome/06. Meet the Careers Team-cuKecPpZ7PM.mp4 10.12 MB
    Part 02-Module 03-Lesson 02_Localization Overview/28. Localization Summary-MVbo4OAgQCc.mp4 10.12 MB
    Part 02-Module 03-Lesson 02_Localization Overview/33. Cancer Test-SZ6Jg1wS604.mp4 10.06 MB
    Part 02-Module 03-Lesson 05_Particle Filters/09. Moving Robot-SFcHsK2SWrI.mp4 10.05 MB
    Part 05-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.mp4 9.98 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/34. Outro-G3soGuQeHGU.mp4 9.97 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/03. L9 03 L GPUs-8eP2EpfBli0.mp4 9.94 MB
    Part 02-Module 03-Lesson 05_Particle Filters/22. Orientation 2-Ex0su1DnIuw.mp4 9.89 MB
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.mp4 9.8 MB
    Part 03-Module 02-Lesson 02_Prediction/01. 01 L Introduction And Overview-aHmVFZ6hMjc.mp4 9.77 MB
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/02. Introduction To Deep Learning-7xRwuECaXBs.mp4 9.77 MB
    Part 02-Module 03-Lesson 02_Localization Overview/26. Sense and Move-1s2dRczcu1A.mp4 9.74 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/04. Transfer Learning-pkCUxzJNtfI.mp4 9.64 MB
    Part 02-Module 03-Lesson 05_Particle Filters/19. New Particle-AROtzVxDDx4.mp4 9.6 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/01. Introduction-Vnj2VNQROtI.mp4 9.59 MB
    Part 03-Module 05-Lesson 05_System Integration Project/02. L5 01 L Project Introduction-UT34zkxfS_M.mp4 9.57 MB
    Part 02-Module 03-Lesson 02_Localization Overview/20. Inexact Motion 2-jR7FERpsqe4.mp4 9.54 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/03. ND013 01 L Intro To Neural Networks-UIycORUrPww.mp4 9.51 MB
    Part 03-Module 04-Lesson 02_Introduction to Functional Safety/01. L1 01 L Introduction To Module-cHadgZtuDZA.mp4 9.5 MB
    Part 04-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.49 MB
    Part 04-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.49 MB
    Part 04-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.49 MB
    Part 01-Module 04-Lesson 07_Decision Trees/08. Coding A Decision Tree-cxV6OAxCfIQ.mp4 9.47 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/15. New Mean and Variance-yo8jf0U4hlc.mp4 9.46 MB
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.mp4 9.46 MB
    Part 03-Module 02-Lesson 01_Search/13. Print Path-CyQ2gl-9W4o.mp4 9.33 MB
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-ASUXN9Ay35M.mp4 9.32 MB
    Part 03-Module 02-Lesson 01_Search/13. Print Path-6UJFZf40aBg.mp4 9.29 MB
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    Part 01-Module 03-Lesson 09_Transfer Learning/07. Alexnet-X-QVsH27Mo4.mp4 8.67 MB
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    Part 01-Module 04-Lesson 02_Gradients and Color Spaces/08. Color Spaces and Thresholding-dMI_so4P1Jc.mp4 8.46 MB
    Part 03-Module 05-Lesson 04_Writing ROS Nodes/08. L3 Arm Mover The Code-0Li845bwxyM.mp4 8.45 MB
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    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.mp4 8.24 MB
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    Part 05-Module 02-Lesson 01_GitHub Profile Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 6.92 MB
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    Part 06-Module 01-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.mp4 6.45 MB
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    Part 02-Module 03-Lesson 01_Introduction to Localization/02. Localization Introduction-lwmXQ-kxU3s.mp4 6.1 MB
    Part 06-Module 01-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.mp4 6.1 MB
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    Part 06-Module 01-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.mp4 6.04 MB
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/15. Radar Measurements-LOz9AaHvB8M.mp4 6.02 MB
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    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/03. What Is Deep Learning-INt1nULYPak.mp4 5.78 MB
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    Part 03-Module 05-Lesson 04_Writing ROS Nodes/12. Look Away The Code -pOZW8SdyYsk.mp4 5.75 MB
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    Part 02-Module 04-Lesson 01_PID Control/01. Intro-CelGYr2DgpI.mp4 5.64 MB
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    Part 06-Module 01-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.mp4 5.61 MB
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    Part 06-Module 01-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.mp4 5.02 MB
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    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/15. L6 22 L Lesson Outro-zSyoNGrZZ0k.mp4 5 MB
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    Part 02-Module 02-Lesson 01_Introduction and Sensors/04. Lidar Strengths And Weaknesses-xpUduMU9-jo.mp4 4.89 MB
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    Part 06-Module 01-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.mp4 3.28 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/07. From Scatterplots to Predictions-dGS0SKu1ox0.mp4 3.27 MB
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.mp4 3.26 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/16. Separating with the New Feature--_jNi_5zEEQ.mp4 3.26 MB
    Part 03-Module 03-Lesson 03_Scene Understanding/13. Outro-vyNI5hdMigs.mp4 3.25 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/19. Playing Around with Kernel Choices-sy_jiSEy-Nw.mp4 3.24 MB
    Part 02-Module 03-Lesson 05_Particle Filters/03. Belief Modality-5vdbYPc7tWw.mp4 3.22 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/01. Welcome to SVM-gnAmmyQ_ZcQ.mp4 3.21 MB
    Part 03-Module 02-Lesson 03_Behavior Planning/08. 07 L StatesForASelfDrivingCar-zoN0-IPe0I4.mp4 3.21 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/32. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.2 MB
    Part 03-Module 05-Lesson 02_Introduction to ROS/21. Recap-7WOQ89HYhxA.mp4 3.2 MB
    Part 03-Module 02-Lesson 02_Prediction/06. 07 L TrajectoryClustering2 - Online Prediction-UPiED4soM4w.mp4 3.18 MB
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/29. 1x1 Convolutions-Zmzgerm6SjA.mp4 3.16 MB
    Part 06-Module 01-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.mp4 3.15 MB
    Part 02-Module 03-Lesson 02_Localization Overview/05. Uniform Distribution-ysebYA6tDZ4.mp4 3.15 MB
    Part 02-Module 03-Lesson 06_Implementation of a Particle Filter/02. Lesson Introduction-_VjhAIChVcI.mp4 3.15 MB
    Part 02-Module 03-Lesson 05_Particle Filters/10. Add Noise-FQEeI3qzaOM.mp4 3.14 MB
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/11. Nd113 C6 L3 09 L Trigonometric Ratios V2-wquwvrT9g_U.mp4 3.12 MB
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.mp4 3.11 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/36. Layers-pg99FkXYK0M.mp4 3.11 MB
    Part 01-Module 04-Lesson 08_Object Detection/26. Train a Classifier-EBEN6KLQm8A.mp4 3.1 MB
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/22. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4 3.09 MB
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.09 MB
    Part 03-Module 02-Lesson 03_Behavior Planning/04. 03 L The Behavior Problem-5t-oVAZagT8.mp4 3.08 MB
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/23. Evaluating The Performance-1HieeV8IUv8.mp4 3.08 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/11. Parameter Update-Lwn6FJgyyYI.mp4 3.07 MB
    Part 02-Module 03-Lesson 03_Markov Localization/03. ND013 M4 L3 02 L Localization Posterior-WCva9DtGgGA.mp4 3.05 MB
    Part 02-Module 04-Lesson 01_PID Control/06. Oscillations - Artificial Intelligence for Robotics-CO3zjkxBaIc.mp4 3.03 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/11. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.03 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/25. Quiz - Cross 1--xxrisIvD0E.mp4 3.02 MB
    Part 03-Module 04-Lesson 02_Introduction to Functional Safety/07. Introduction To Evaluating Risks-3N-6YK_pPzA.mp4 3.01 MB
    Part 03-Module 03-Lesson 02_Fully Convolutional Networks/03. Fully Convolutional Networks-_Lh2ozg5yTs.mp4 3 MB
    Part 06-Module 01-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.mp4 2.99 MB
    Part 03-Module 04-Lesson 04_Functional Safety Hazard Analysis and Risk Assessment/07. L3 09 L HARA Identification Of Hazards-LXp7ScZaKp4.mp4 2.97 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/29. Assignment UKF Update-f36o4sCEQvY.mp4 2.94 MB
    Part 03-Module 02-Lesson 03_Behavior Planning/01. 06 Path Planning A03 Where To--9ZrXlxPH4L4.mp4 2.93 MB
    Part 02-Module 03-Lesson 03_Markov Localization/15. Explain Bayes Rule And Apply Law Of Total Probability-p2qfHa9G7_k.mp4 2.93 MB
    Part 02-Module 03-Lesson 05_Particle Filters/09. Moving Robot-_37pf6lV15s.mp4 2.92 MB
    Part 03-Module 04-Lesson 06_Functional Safety Technical Safety Concept/07. L5 14 L Outro-sIe4SZfDUmM.mp4 2.92 MB
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 2.9 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/17. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.87 MB
    Part 03-Module 04-Lesson 05_Functional Safety Functional Safety Concept/08. L4 15 Fault Tolerant Time Interval-o4PzRfVN_to.mp4 2.86 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/38. Multilayer perceptrons-Rs9petvTBLk.mp4 2.85 MB
    Part 03-Module 02-Lesson 02_Prediction/03. 04 L Model Vs Data Driven Approaches-ehfA_NC7Ka4.mp4 2.85 MB
    Part 01-Module 01-Lesson 03_Computer Vision Fundamentals/03. color selection-bNOWJ9wdmhk.mp4 2.84 MB
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.mp4 2.83 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/36. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 2.83 MB
    Part 03-Module 02-Lesson 04_Trajectory Generation/03. 02 L The Motion Planning Problem-daGIOru4Bi4.mp4 2.82 MB
    Part 06-Module 01-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.mp4 2.81 MB
    Part 06-Module 01-Lesson 08_Technical Interviewing Techniques/09. Interview Wrap-Up-sz4Ekcu9a_Q.mp4 2.8 MB
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/08. L6 11-12 Software Safety Robustness And Quality-RQEnvtti3sM.mp4 2.8 MB
    Part 06-Module 01-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.mp4 2.78 MB
    Part 02-Module 03-Lesson 07_Kidnapped Vehicle Project/01. 05 Localization A01 Sparse Localization-DBZ7QdXPldc.mp4 2.78 MB
    Part 03-Module 04-Lesson 02_Introduction to Functional Safety/09. L1 19 Introduction To Iso26262-bjvpmBOG60Q.mp4 2.77 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/12. Participating in open source projects-OxL-gMTizUA.mp4 2.77 MB
    Part 03-Module 02-Lesson 03_Behavior Planning/12. 12 L CreateACostFunctionSpeedPenalty-wGRDT2wTnn8.mp4 2.75 MB
    Part 02-Module 04-Lesson 04_Model Predictive Control/11. Model Predictive Control Outro-iEdMInAsjgM.mp4 2.73 MB
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/13. Training Your Logistic Classifier-WQsdr1EJgz8.mp4 2.72 MB
    Part 02-Module 03-Lesson 03_Markov Localization/16. Explain Law Of Total Probability And Markov Assumption-9hGU7s5m8c0.mp4 2.71 MB
    Part 03-Module 03-Lesson 04_Inference Performance/01. Intro-dK5Yn3sq5RM.mp4 2.7 MB
    Part 01-Module 03-Lesson 04_Deep Neural Networks/11. 10 Backprop-wSXcrBbY8oE.mp4 2.68 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/15. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.68 MB
    Part 03-Module 02-Lesson 01_Search/09. Maze-yVh0lVlerWs.mp4 2.68 MB
    Part 03-Module 05-Lesson 03_Packages & Catkin Workspaces/01. 08 System Integration A04 ROS At Stanford-YxbwPjGylI8.mp4 2.68 MB
    Part 01-Module 04-Lesson 07_Decision Trees/16. Entropy Calculation Part 1-K-rQ8KnmmH8.mp4 2.68 MB
    Part 02-Module 03-Lesson 06_Implementation of a Particle Filter/10. Data Association- Nearest Neighbor-nG_pFGT-fuo.mp4 2.67 MB
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/06. Nd113 C6 L3 04 L Moving At An Angle V2-2KDq_ZzN3Mk.mp4 2.66 MB
    Part 03-Module 04-Lesson 06_Functional Safety Technical Safety Concept/04. L5 09 Technical Safety Requirement Attributes-EOYTl2e8wEs.mp4 2.66 MB
    Part 02-Module 03-Lesson 03_Markov Localization/02. Markov Location Lesson Overview-rSj5lpzliQg.mp4 2.66 MB
    Part 01-Module 04-Lesson 04_Advanced Lane Finding Project/01. 02 Computer Vision A02 Becoming An Expert--ZIJqfTk8mg.mp4 2.65 MB
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/30. Inception Module-SlTm03bEOxA.mp4 2.62 MB
    Part 03-Module 04-Lesson 03_Functional Safety Safety Plan/03. L2 05 L Tailoring The Safety Lifecycle-Ym9raP5zb2U.mp4 2.59 MB
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.mp4 2.59 MB
    Part 01-Module 03-Lesson 07_Traffic Sign Classifier Project/01. 03 Deep Learning A03 Starting With Neural Networks-TgRhLmHRvfE.mp4 2.59 MB
    Part 01-Module 04-Lesson 01_Camera Calibration/01. 02 Computer Vision A01 The Challenges With Cameras-n2RSEjPn814.mp4 2.59 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/11. 09 Higher Dimensions-eBHunImDmWw.mp4 2.59 MB
    Part 01-Module 03-Lesson 04_Deep Neural Networks/19. Dropout-6DcImJS8uV8.mp4 2.58 MB
    Part 03-Module 02-Lesson 04_Trajectory Generation/24. 23 L DerivationOverview-TuVp_HhQq7A.mp4 2.58 MB
    Part 03-Module 03-Lesson 02_Fully Convolutional Networks/02. Why Fully Convolutional Networks (FCNs) -WQ_YOz1o9GM.mp4 2.57 MB
    Part 01-Module 03-Lesson 04_Deep Neural Networks/13. Training a Deep Learning Network-CsB7yUtMJyk.mp4 2.56 MB
    Part 02-Module 03-Lesson 05_Particle Filters/12. Creating Particles-JNI9O9FjfDQ.mp4 2.54 MB
    Part 02-Module 03-Lesson 05_Particle Filters/05. Exact or Approximate-WKlm2aO2QGY.mp4 2.54 MB
    Part 03-Module 02-Lesson 04_Trajectory Generation/30. 29 L Implementing Feasibility-8tD8Os9_gKc.mp4 2.54 MB
    Part 06-Module 01-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.mp4 2.52 MB
    Part 03-Module 03-Lesson 03_Scene Understanding/04. Scene Understanding-aMQREc-mP50.mp4 2.52 MB
    Part 03-Module 04-Lesson 04_Functional Safety Hazard Analysis and Risk Assessment/09. L3 18 L HARA ASIL Levels-l7vx-w06fZw.mp4 2.5 MB
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-sCZI5gWS6mg.mp4 2.49 MB
    Part 02-Module 03-Lesson 02_Localization Overview/20. Inexact Motion 2-gZbPZLFKS68.mp4 2.49 MB
    Part 03-Module 04-Lesson 08_Elective Project Functional Safety/01. L6 25 L Project Outro-k3tl3pkGBa8.mp4 2.48 MB
    Part 01-Module 04-Lesson 08_Object Detection/21. Combining Features-5tQx6J-VzsI.mp4 2.47 MB
    Part 06-Module 01-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.mp4 2.47 MB
    Part 03-Module 05-Lesson 03_Packages & Catkin Workspaces/08. ROS L2 Recap-OIZsHXuHWuI.mp4 2.46 MB
    Part 02-Module 03-Lesson 03_Markov Localization/32. Finalize The Bayes Localization Filter-teVw2J-_6ZE.mp4 2.45 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/20. Assignment Sigma Point Prediction-RQvnRpSPUak.mp4 2.45 MB
    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-TkaTTptnYdA.mp4 2.45 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-U3FUxkm1MxI.mp4 2.45 MB
    Part 03-Module 05-Lesson 03_Packages & Catkin Workspaces/04. Adding a Package-UJlCdokCJJ0.mp4 2.44 MB
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/06. Nd113 C6 L3 045 L Moving At An Angle Part2 V2-iI6zCp0RegM.mp4 2.43 MB
    Part 02-Module 03-Lesson 03_Markov Localization/01. 05 Localization A02 A Return To Bayes Ruls-7FgzbyeJCp8.mp4 2.4 MB
    Part 03-Module 03-Lesson 04_Inference Performance/16. Outro-jMSB5_P_fss.mp4 2.38 MB
    Part 03-Module 04-Lesson 04_Functional Safety Hazard Analysis and Risk Assessment/08. L3 14 HARA Risk Assessment, Severity, Exposure, Controllability-44NYK53gOAM.mp4 2.38 MB
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/10. L6 15 Freedom From Interference Temporal-ChGZCPXko7M.mp4 2.38 MB
    Part 03-Module 02-Lesson 03_Behavior Planning/23. Toby Ben Outro-Hzk5-lezrJk.mp4 2.37 MB
    Part 02-Module 03-Lesson 02_Localization Overview/24. Move Twice-sKiumVTdpgY.mp4 2.37 MB
    Part 01-Module 03-Lesson 04_Deep Neural Networks/04. Linear Models Are Limited-12AYOYDrpfQ.mp4 2.37 MB
    Part 03-Module 02-Lesson 01_Search/06. Compute Cost 2-n9_th4V4qE4.mp4 2.36 MB
    Part 02-Module 03-Lesson 03_Markov Localization/41. Conclusion-3npZxfdrOpY.mp4 2.36 MB
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/01. 03 Deep Learning A05 Deep Learning Foundations-Fw6cM2mpfcs.mp4 2.36 MB
    Part 02-Module 03-Lesson 05_Particle Filters/18. Never Sampled 3-Z1oQl-1cUeE.mp4 2.35 MB
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.mp4 2.34 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/09. 13 Q CTRV Process Noise Position-DJ_K1udemNk.mp4 2.34 MB
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/16. L6 24 L Module Outro-QT-4yIV9dEM.mp4 2.34 MB
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/06. L6 09 Software Safety Requirments Architecture Testing And Intergration-OJMGRtJciNI.mp4 2.33 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/22. 26 L Predict Mean And Covar-6DELFN7Fz4c.mp4 2.33 MB
    Part 06-Module 01-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.mp4 2.32 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/04. CTRV-o2HVZFSH1Fs.mp4 2.31 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/02. Separating Line-mzKPXz-Yhwk.mp4 2.31 MB
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/06. ND013 M3 L4 05 L Kalman Filter Equations In C++-ZG8Ya-mCGhI.mp4 2.31 MB
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/33. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4 2.3 MB
    Part 03-Module 02-Lesson 03_Behavior Planning/20. 16 L SchedulingComputeTime-N6AlIUczqRM.mp4 2.3 MB
    Part 03-Module 04-Lesson 05_Functional Safety Functional Safety Concept/06. L4 10 Function Safety Requirements And ASIL Inheritance-oeKSXaP7Lxg.mp4 2.29 MB
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/07. Nd113 C6 L3 05 L Moving At 53 Degrees V2-VmoknN6xLKs.mp4 2.28 MB
    Part 03-Module 02-Lesson 05_Path Planning Project/01. 06 Path Planning A02 Planning Is Hard-15yIDPNbmWc.mp4 2.28 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/12. NB Decision Boundary in Python-pauohSxuCVs.mp4 2.27 MB
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/09. State Prediction-_A0NRvmgo3w.mp4 2.27 MB
    Part 03-Module 03-Lesson 03_Scene Understanding/01. Intro-z036GNuoiBk.mp4 2.27 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/21. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.26 MB
    Part 03-Module 05-Lesson 02_Introduction to ROS/14. Sourcing The ROS Environment-6cHlu-KVi98.mp4 2.26 MB
    Part 02-Module 03-Lesson 01_Introduction to Localization/06. Overview of the Lessons-KzvW0gkYOgo.mp4 2.26 MB
    Part 05-Module 02-Lesson 01_GitHub Profile Review/07. Quick Fixes #2-It6AEuSDQw0.mp4 2.25 MB
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/11. L6 17 Freedom From Interference Temporal Part 2-wRbGk0SwWNQ.mp4 2.25 MB
    Part 02-Module 03-Lesson 05_Particle Filters/21. Orientation 1-cupiUHaKvdI.mp4 2.24 MB
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/34. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4 2.23 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/06. Evaluate Gaussian-4-0nBfsD4jo.mp4 2.23 MB
    Part 02-Module 03-Lesson 05_Particle Filters/19. New Particle-LJXbHoq5EZk.mp4 2.23 MB
    Part 03-Module 02-Lesson 04_Trajectory Generation/07. 06 L A- Reminder Solution-HtQjw7qr2-o.mp4 2.22 MB
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/04. Statistical Invariance-0Hr5YwUUhr0.mp4 2.22 MB
    Part 02-Module 04-Lesson 01_PID Control/03. Proportional Control - Artificial Intelligence for Robotics-gGo-gSFqYqg.mp4 2.2 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/07. SVM Response to Outliers-w-czJptEyBk.mp4 2.19 MB
    Part 02-Module 03-Lesson 04_Motion Models/03. Motion Models-B2bXg8LaeF0.mp4 2.19 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/12. UKF Basics Unscented Transformation-8jbckHQDl4A.mp4 2.17 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/07. SVM Response to Outliers-TEAGqUkQVdM.mp4 2.16 MB
    Part 01-Module 03-Lesson 06_LeNet for Traffic Signs/07. LeNet Model Training-9580p7ZRQVY.mp4 2.16 MB
    Part 01-Module 04-Lesson 07_Decision Trees/20. Entropy Calculation Part 5-B_fHrMIzIgE.mp4 2.15 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/34. Non-Linear Data-F7ZiE8PQiSc.mp4 2.14 MB
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/28. UKF Update-pJ5XauGNclI.mp4 2.14 MB
    Part 06-Module 01-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.mp4 2.14 MB
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-CIxvkVy1UBI.mp4 2.13 MB
    Part 02-Module 04-Lesson 01_PID Control/08. PD Controller Solution - Artificial Intelligence for Robotics-YgomQgfFlTQ.mp4 2.12 MB
    Part 02-Module 03-Lesson 06_Implementation of a Particle Filter/13. Calculating Error-HiRrJYZr-0I.mp4 2.12 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/08. SVM Outlier Practice-WxAO6ByCvew.mp4 2.12 MB
    Part 02-Module 03-Lesson 02_Localization Overview/22. Inexact Move Function-68Kao9dkIKA.mp4 2.11 MB
    Part 03-Module 05-Lesson 02_Introduction to ROS/01. 08 System Integration A03 Communication Between Systems-a9hYIvymlkQ.mp4 2.11 MB
    Part 01-Module 04-Lesson 08_Object Detection/06. Features-u3NOabeuMjA.mp4 2.1 MB
    Part 03-Module 03-Lesson 04_Inference Performance/09. Reducing Precision-bZPG5I_igR8.mp4 2.1 MB
    Part 01-Module 04-Lesson 08_Object Detection/17. Gradient Features-cvGtDBu8ONQ.mp4 2.09 MB
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/13. Trigonometry And Vehicle Motion-WY3T-9GHI_0.mp4 2.09 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/27. Formula For Cross 1-qvr_ego_d6w.mp4 2.08 MB
    Part 03-Module 03-Lesson 04_Inference Performance/04. P2-Yr_rRyEOTnM.mp4 2.08 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/08. Classsification Example-Dh625piH7Z0.mp4 2.07 MB
    Part 03-Module 03-Lesson 02_Fully Convolutional Networks/12. Outro-ESIl11NfQ7Q.mp4 2.06 MB
    Part 03-Module 02-Lesson 03_Behavior Planning/06. 05 L Formalizing FSMs-sEZn3iZgOaI.mp4 2.06 MB
    Part 01-Module 01-Lesson 03_Computer Vision Fundamentals/01. 01 Introduction A01 Power Of Cameras-lCPWJEEzUeo.mp4 2.06 MB
    Part 06-Module 01-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.mp4 2.06 MB
    Part 03-Module 04-Lesson 03_Functional Safety Safety Plan/05. L2 08 L Development Interface Agreement-xF79RP2LduY.mp4 2.06 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-gbkORDbJM50.mp4 2.06 MB
    Part 02-Module 02-Lesson 02_Kalman Filters/22. Another Prediction-cUKlYjQEQGY.mp4 2.05 MB
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/16. Calculating NB Accuracy-m989etSymQQ.mp4 2.03 MB
    Part 03-Module 04-Lesson 04_Functional Safety Hazard Analysis and Risk Assessment/04. L3 04 L Introduction To HARA-Qxui0XShsbE.mp4 2.03 MB
    Part 03-Module 04-Lesson 06_Functional Safety Technical Safety Concept/02. L5 03 Deriving Techincal Safety Requirements From Functional Safety Requirements-fVLsG83a-So.mp4 2.02 MB
    Part 03-Module 04-Lesson 05_Functional Safety Functional Safety Concept/09. L4 18 Warning And Degradation Concept-khNhy3IwKa0.mp4 2.01 MB
    Part 02-Module 02-Lesson 05_Extended Kalman Filter Project/01. 04 Sensor Fusion A03 Back To Bayes Theorem-wel0ggSIT54.mp4 2.01 MB
    Part 02-Module 03-Lesson 02_Localization Overview/24. Move Twice-oqlgQa1IdcY.mp4 1.99 MB
    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.mp4 1.99 MB
    Part 02-Module 04-Lesson 01_PID Control/10. Is PD Enough - Artificial Intelligence for Robotics-gDbpwPdStlY.mp4 1.99 MB
    Part 01-Module 04-Lesson 06_Support Vector Machines/12. Nonlinear SVMs-6UgInp_gf1w.mp4 1.98 MB
    Part 01-Module 03-Lesson 09_Transfer Learning/img/03-gpus.png 1.97 MB
    Part 01-Module 04-Lesson 07_Decision Trees/30. Information Gain Calculation Part 9-V-jzhJoeZj8.mp4 1.96 MB
    Part 03-Module 05-Lesson 02_Introduction to ROS/07. Message Passing-IpNp13F-TgQ.mp4 1.96 MB
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/13. L6 18 Freedom From Interference Communication-J2T842SLPgs.mp4 1.96 MB
    Part 02-Module 04-Lesson 01_PID Control/16. Outro-zF_MUxjCl04.mp4 1.96 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/22. DL 18 S Softmax-n8S-v_LCTms.mp4 1.95 MB
    Part 02-Module 03-Lesson 02_Localization Overview/25. Move 1000-nYt9b_pNvEE.mp4 1.94 MB
    Part 02-Module 03-Lesson 02_Localization Overview/27. Sense and Move 2--wT7h9Gdm_8.mp4 1.93 MB
    Part 01-Module 04-Lesson 07_Decision Trees/28. Information Gain Calculation Part 7-EDFp4wU5BMo.mp4 1.93 MB
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/16. Perceptron Algorithm--zhTROHtscQ.mp4 1.92 MB
    Part 01-Module 04-Lesson 07_Decision Trees/27. Information Gain Calculation Part 6-zqmrW9N9WGw.mp4 1.92 MB
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-TF6AWXSlOcY.mp4 1.92 MB
    Part 01-Module 03-Lesson 06_LeNet for Traffic Signs/01. LeNet Architecture-FQYrzmnbsMs.mp4 1.91 MB
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/22. Sensor Fusion General Processing Flow-dcTY4vRg5vo.mp4 1.91 MB
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    Part 02-Module 04-Lesson 03_Vehicle Models/15. Dynamic Models - Part 4 Tire Models-OFIL0yqsV7o.zh-CN.vtt 853 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-gbkORDbJM50.ja.vtt 853 B
    Part 01-Module 04-Lesson 02_Gradients and Color Spaces/07. Color Spaces-mhILAhzgPRE.zh-CN.vtt 851 B
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-hzDsYZ61D5M.es-ES.vtt 851 B
    Part 03-Module 03-Lesson 05_Semantic Segmentation Project/01. Introduction-qA6Za_Pt5d0.en.vtt 850 B
    Part 01-Module 04-Lesson 01_Camera Calibration/01. 02 Computer Vision A01 The Challenges With Cameras-n2RSEjPn814.en.vtt 850 B
    Part 03-Module 03-Lesson 02_Fully Convolutional Networks/07. Transposed Convolutions-K6mlLX8ZZDs.zh-CN.vtt 850 B
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/08. Kalman Filter Equations In C++ Programming-smRjTGQG2SY.zh-CN.vtt 849 B
    Part 06-Module 01-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.zh-CN.vtt 849 B
    Part 02-Module 03-Lesson 01_Introduction to Localization/03. Localization Intuition-mVCSCU67D80.en.vtt 848 B
    Part 03-Module 04-Lesson 06_Functional Safety Technical Safety Concept/07. L5 14 L Outro-sIe4SZfDUmM.en.vtt 847 B
    Part 02-Module 02-Lesson 02_Kalman Filters/07. Maximize Gaussian-fRYtUP0P4Lg.en.vtt 846 B
    Part 03-Module 03-Lesson 04_Inference Performance/03. L3 03 L Semantic Segmentation Revisited-xFcI26kLtiY.en.vtt 846 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/07. SVM Response to Outliers-TEAGqUkQVdM.en.vtt 846 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/31. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt 845 B
    Part 01-Module 04-Lesson 07_Decision Trees/10. Decision Tree Parameters-Is5T4alCCGQ.zh-CN.vtt 845 B
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/26. Assignment Predict Radar Measurement-GYQeizoj09E.zh-CN.vtt 844 B
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/01. L6 01 L Intro-urgjYwIY3hs.zh-CN.vtt 844 B
    Part 03-Module 03-Lesson 03_Scene Understanding/04. Scene Understanding-aMQREc-mP50.zh-CN.vtt 844 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/13. Nonlinear Data-EllzeBecnkU.en.vtt 843 B
    Part 03-Module 02-Lesson 01_Search/06. Compute Cost 2-OXIESpN0KaE.ru.vtt 843 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/02. Intro To Deep Neural Networks-SXtXg_BB4lI.en.vtt 842 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/20. Kernel and Gamma-znlTyocTgSc.ja.vtt 842 B
    Part 02-Module 04-Lesson 03_Vehicle Models/12. Dynamic Models - Part 1 Forces-KRN7GVJkFnU.zh-CN.vtt 841 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/04. Maximizing the Margin-otAraUuSrJo.en.vtt 837 B
    Part 02-Module 03-Lesson 05_Particle Filters/05. Exact or Approximate-1N3_RnTDFqU.zh-CN.vtt 836 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.bn.vtt 833 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/10. ChainRule-DxOg_olir0k.pt-BR.vtt 833 B
    Part 02-Module 04-Lesson 03_Vehicle Models/03. State-6vFczwAYjsU.zh-CN.vtt 832 B
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.es-MX.vtt 832 B
    Part 02-Module 04-Lesson 01_PID Control/03. Proportional Control - Artificial Intelligence for Robotics-gGo-gSFqYqg.ja.vtt 831 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/08. SVM Outlier Practice-WxAO6ByCvew.ar.vtt 830 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-e21oU80gwWc.bn.vtt 830 B
    Part 02-Module 02-Lesson 02_Kalman Filters/10. Predicting the Peak-PsyqM704q2Y.es-ES.vtt 829 B
    Part 02-Module 04-Lesson 04_Model Predictive Control/03. Dealing With Stopping-2gkRWj7KIMU.en.vtt 829 B
    Part 03-Module 02-Lesson 01_Search/15. Implement A-V0Ppaw5G2Pg.en.vtt 827 B
    Part 02-Module 03-Lesson 03_Markov Localization/12. 03.5 S How Much Data-PQV6gWuyVOs.en.vtt 827 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/01. 03 Deep Learning A05 Deep Learning Foundations-Fw6cM2mpfcs.en.vtt 826 B
    Part 03-Module 02-Lesson 05_Path Planning Project/01. 06 Path Planning A02 Planning Is Hard-15yIDPNbmWc.en.vtt 826 B
    Part 03-Module 02-Lesson 03_Behavior Planning/23. Toby Ben Outro-Hzk5-lezrJk.zh-CN.vtt 826 B
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-hzDsYZ61D5M.pt-PT.vtt 825 B
    Part 01-Module 03-Lesson 02_MiniFlow/01. Introduction to MiniFlow-FxmB3Q308h0.en.vtt 825 B
    Part 02-Module 03-Lesson 05_Particle Filters/25. Filters-d_DXbkU7iPY.ja.vtt 825 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/24. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt 823 B
    Part 01-Module 03-Lesson 09_Transfer Learning/17. Alexnet Today-AItZPkRHH_I.zh-CN.vtt 822 B
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-hzDsYZ61D5M.pt.vtt 822 B
    Part 02-Module 02-Lesson 02_Kalman Filters/05. Preferred Gaussian--9AVZ-N_gbM.zh-CN.vtt 821 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-DV2cX9W0tT8.es-ES.vtt 819 B
    Part 02-Module 03-Lesson 03_Markov Localization/41. Conclusion-3npZxfdrOpY.zh-CN.vtt 818 B
    Part 01-Module 04-Lesson 07_Decision Trees/01. Welcome To Decision Trees-5eAHVk1-Hz0.zh-CN.vtt 817 B
    Part 06-Module 01-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.pt-BR.vtt 817 B
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/08. CTRV Process Noise Effect Last 3-DUm8e7K8qZ8.zh-CN.vtt 817 B
    Part 03-Module 05-Lesson 04_Writing ROS Nodes/16. L4 01 L Outro-LwI4UmDGLeM.en.vtt 815 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/19. Congrats on Learning Naive Bayes-nQsYfzO7-00.zh-CN.vtt 814 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/25. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/16. Separating with the New Feature--_jNi_5zEEQ.en.vtt 813 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt 812 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/16. Separating with the New Feature--_jNi_5zEEQ.ja.vtt 812 B
    Part 01-Module 04-Lesson 08_Object Detection/06. Features-u3NOabeuMjA.en.vtt 811 B
    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-6tV5NY1HoNA.it.vtt 810 B
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-hzDsYZ61D5M.it.vtt 810 B
    Part 02-Module 02-Lesson 01_Introduction and Sensors/05. Live Data Walkthrough-zXu6y0aNfjk.en.vtt 810 B
    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-6tV5NY1HoNA.es-ES.vtt 809 B
    Part 03-Module 02-Lesson 03_Behavior Planning/10. 09 Q InputsToTransitionFunctions-8jStt2d_SYc.en.vtt 809 B
    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-6tV5NY1HoNA.zh-CN.vtt 806 B
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    Part 03-Module 03-Lesson 03_Scene Understanding/13. Outro-vyNI5hdMigs.en.vtt 805 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/19. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 B
    Part 02-Module 03-Lesson 03_Markov Localization/23. ND013 M4 L3 15.5 Q Noise In Motion Model-zRbT36RTlhs.zh-CN.vtt 804 B
    Part 03-Module 05-Lesson 01_Autonomous Vehicle Architecture/06. L1 08 L Planning Subsystem-MxG9DtKiSqM.zh-CN.vtt 804 B
    Part 03-Module 05-Lesson 03_Packages & Catkin Workspaces/01. 08 System Integration A04 ROS At Stanford-YxbwPjGylI8.en.vtt 802 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-daVA3PI2E6o.pt-BR.vtt 802 B
    Part 02-Module 03-Lesson 05_Particle Filters/11. Robot World-qq5h-Xw4DGg.en.vtt 800 B
    Part 03-Module 02-Lesson 03_Behavior Planning/10. 09 Q InputsToTransitionFunctions-8jStt2d_SYc.zh-CN.vtt 799 B
    Part 02-Module 03-Lesson 05_Particle Filters/25. Filters-d_DXbkU7iPY.zh-CN.vtt 799 B
    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-6tV5NY1HoNA.en.vtt 798 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-gbkORDbJM50.en.vtt 798 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-gbkORDbJM50.pt-BR.vtt 798 B
    Part 02-Module 02-Lesson 02_Kalman Filters/26. Conclusion-6kFMxhlfHuI.zh-CN.vtt 797 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/01. Welcome to SVM-gnAmmyQ_ZcQ.pt-BR.vtt 797 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/13. Nonlinear Data-EllzeBecnkU.zh-CN.vtt 797 B
    Part 02-Module 03-Lesson 05_Particle Filters/25. Filters-d_DXbkU7iPY.en.vtt 796 B
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-hzDsYZ61D5M.zh-CN.vtt 795 B
    Part 02-Module 04-Lesson 01_PID Control/03. Proportional Control - Artificial Intelligence for Robotics-gGo-gSFqYqg.en.vtt 795 B
    Part 02-Module 03-Lesson 05_Particle Filters/10. Add Noise-ajOKsQLxoJI.zh-CN.vtt 794 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/23. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt 793 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/03. Choosing Between Separating Lines-swoZxkrxIB0.ar.vtt 793 B
    Part 02-Module 02-Lesson 01_Introduction and Sensors/05. Live Data Walkthrough-zXu6y0aNfjk.zh-CN.vtt 792 B
    Part 04-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.en.vtt 791 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/19. Error Functions-YfUUunxWIJw.en.vtt 790 B
    Part 02-Module 03-Lesson 02_Localization Overview/15. Multiple Measurements-gDO4sF8gR9k.es-ES.vtt 789 B
    Part 03-Module 02-Lesson 01_Search/06. Compute Cost 2-n9_th4V4qE4.zh-CN.vtt 788 B
    Part 01-Module 03-Lesson 08_Keras/04. High Level Frameworks-ThmsQxazSvM.en.vtt 787 B
    Part 02-Module 03-Lesson 02_Localization Overview/15. Multiple Measurements-gDO4sF8gR9k.es.vtt 787 B
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    Part 01-Module 04-Lesson 06_Support Vector Machines/04. Maximizing the Margin-otAraUuSrJo.zh-CN.vtt 786 B
    Part 02-Module 03-Lesson 02_Localization Overview/15. Multiple Measurements-gDO4sF8gR9k.it.vtt 785 B
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-hzDsYZ61D5M.en.vtt 784 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-erdekkpG-Do.ar.vtt 783 B
    Part 01-Module 04-Lesson 07_Decision Trees/14. Minimizing Impurity in Splitting-L6J6BRFgDiI.ja.vtt 783 B
    Part 01-Module 04-Lesson 08_Object Detection/23. Build a Classifier-YUCFFNC7tw4.en.vtt 782 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/06. Speed Scatterplot 3-4qJwfAWG_wQ.zh-CN.vtt 780 B
    Part 01-Module 03-Lesson 07_Traffic Sign Classifier Project/01. 03 Deep Learning A03 Starting With Neural Networks-TgRhLmHRvfE.en.vtt 780 B
    Part 02-Module 03-Lesson 02_Localization Overview/15. Multiple Measurements-gDO4sF8gR9k.zh-CN.vtt 779 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/07. From Scatterplots to Predictions-dGS0SKu1ox0.zh-CN.vtt 779 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/02. Intro To Deep Neural Networks-SXtXg_BB4lI.zh-CN.vtt 779 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/08. SVM Outlier Practice-osn2fVnCVgQ.zh-CN.vtt 778 B
    Part 01-Module 04-Lesson 07_Decision Trees/14. Minimizing Impurity in Splitting-lfZg7j5W7u8.zh-CN.vtt 778 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/16. Outro-dps7Ti6Lado.en.vtt 777 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/31. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt 777 B
    Part 02-Module 04-Lesson 03_Vehicle Models/07. Following Trajectories-sOSHaAf_7b8.zh-CN.vtt 776 B
    Part 02-Module 04-Lesson 01_PID Control/08. PD Controller Solution - Artificial Intelligence for Robotics-YgomQgfFlTQ.ja.vtt 776 B
    Part 02-Module 04-Lesson 03_Vehicle Models/13. Dynamic Models - Part 2 Slip Angle-oDusBbn820k.zh-CN.vtt 775 B
    Part 02-Module 03-Lesson 05_Particle Filters/13. Robot Particles--HQf6pkcebQ.zh-CN.vtt 774 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-e21oU80gwWc.ru.vtt 774 B
    Part 02-Module 02-Lesson 02_Kalman Filters/10. Predicting the Peak-PsyqM704q2Y.en.vtt 773 B
    Part 02-Module 02-Lesson 05_Extended Kalman Filter Project/01. 04 Sensor Fusion A03 Back To Bayes Theorem-wel0ggSIT54.en.vtt 772 B
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/13. Trigonometry And Vehicle Motion-WY3T-9GHI_0.zh-CN.vtt 772 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/20. Kernel and Gamma-znlTyocTgSc.pt-BR.vtt 772 B
    Part 03-Module 03-Lesson 05_Semantic Segmentation Project/01. Introduction-qA6Za_Pt5d0.zh-CN.vtt 770 B
    Part 06-Module 01-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en-US.vtt 770 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/01. Welcome to SVM-gnAmmyQ_ZcQ.en.vtt 770 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/20. Kernel and Gamma-znlTyocTgSc.en.vtt 770 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/31. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt 769 B
    Part 01-Module 04-Lesson 08_Object Detection/23. Build a Classifier-YUCFFNC7tw4.zh-CN.vtt 768 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/12. Participating in open source projects-OxL-gMTizUA.ar.vtt 768 B
    Part 01-Module 03-Lesson 02_MiniFlow/01. Introduction to MiniFlow-FxmB3Q308h0.zh-CN.vtt 768 B
    Part 02-Module 02-Lesson 02_Kalman Filters/07. Maximize Gaussian-fRYtUP0P4Lg.zh-CN.vtt 767 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-SW_wvez0izo.zh-CN.vtt 767 B
    Part 06-Module 01-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en.vtt 767 B
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/24. Evaluating The Performance-1iVBYQ_KWXk.en.vtt 765 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/09. No Neurons-svA0HOjFFl0.zh-CN.vtt 765 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/24. Numerical Stability-_SbGcOS-jcQ.en-US.vtt 764 B
    Part 03-Module 05-Lesson 04_Writing ROS Nodes/16. L4 01 L Outro-LwI4UmDGLeM.zh-CN.vtt 764 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/05. Rectified Linear Units-2-GirRl7TqU.pt-BR.vtt 763 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-DV2cX9W0tT8.en.vtt 762 B
    Part 03-Module 02-Lesson 01_Search/05. Compute Cost-7-yOaHVeATk.ru.vtt 761 B
    Part 02-Module 03-Lesson 02_Localization Overview/03. Total Probability-n1EacrqyCs8.zh-Hans.vtt 760 B
    Part 04-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.pt-BR.vtt 760 B
    Part 03-Module 03-Lesson 03_Scene Understanding/03. Semantic Segmentation-_L5gJnZrw48.zh-CN.vtt 758 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-sCZI5gWS6mg.ja.vtt 758 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-sCZI5gWS6mg.en.vtt 757 B
    Part 03-Module 02-Lesson 01_Search/09. Maze-ge_-o0RfrgM.ja.vtt 757 B
    Part 02-Module 03-Lesson 03_Markov Localization/24. Noise In Motion Model Solution-zJ9NWz7IlOM.en.vtt 756 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-daVA3PI2E6o.en.vtt 756 B
    Part 02-Module 03-Lesson 02_Localization Overview/35. Coin Flip Quiz-hzDsYZ61D5M.ja.vtt 755 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-gbkORDbJM50.zh-CN.vtt 752 B
    Part 02-Module 03-Lesson 01_Introduction to Localization/03. Localization Intuition-mVCSCU67D80.zh-CN.vtt 751 B
    Part 02-Module 03-Lesson 07_Kidnapped Vehicle Project/01. 05 Localization A01 Sparse Localization-DBZ7QdXPldc.en.vtt 749 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/16. Separating with the New Feature--_jNi_5zEEQ.zh-CN.vtt 749 B
    Part 02-Module 03-Lesson 03_Markov Localization/12. 03.5 S How Much Data-PQV6gWuyVOs.zh-CN.vtt 748 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/07. SVM Response to Outliers-TEAGqUkQVdM.zh-CN.vtt 748 B
    Part 01-Module 04-Lesson 07_Decision Trees/14. Minimizing Impurity in Splitting-L6J6BRFgDiI.zh-CN.vtt 747 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/09. No Neurons-svA0HOjFFl0.en-US.vtt 747 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/09. No Neurons-svA0HOjFFl0.en.vtt 745 B
    Part 03-Module 02-Lesson 01_Search/08. Optimal Path 2-qFswCrEUZSM.ru.vtt 744 B
    Part 01-Module 03-Lesson 08_Keras/04. High Level Frameworks-ThmsQxazSvM.zh-CN.vtt 743 B
    Part 06-Module 01-Lesson 05_Trees/01. Trees-PXie7f22v2Q.zh-CN.vtt 742 B
    Part 02-Module 03-Lesson 05_Particle Filters/11. Robot World-qq5h-Xw4DGg.zh-CN.vtt 740 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/19. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/03. Color-Question-BdQccpMwk80.en.vtt 739 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-sCZI5gWS6mg.pt-BR.vtt 739 B
    Part 02-Module 03-Lesson 03_Markov Localization/11. Quiz How Much Data-wzcFHAf-9lo.en.vtt 737 B
    Part 03-Module 02-Lesson 01_Search/15. Implement A-V0Ppaw5G2Pg.zh-CN.vtt 737 B
    Part 02-Module 03-Lesson 03_Markov Localization/24. Noise In Motion Model Solution-zJ9NWz7IlOM.zh-CN.vtt 730 B
    Part 01-Module 04-Lesson 07_Decision Trees/14. Minimizing Impurity in Splitting-L6J6BRFgDiI.en.vtt 728 B
    Part 01-Module 04-Lesson 08_Object Detection/06. Features-u3NOabeuMjA.zh-CN.vtt 727 B
    Part 03-Module 02-Lesson 01_Search/09. Maze-ge_-o0RfrgM.en.vtt 725 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.bn.vtt 725 B
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/02. CTRV-g72HXEcSQHU.en.vtt 724 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-UX3W8TUKbJ0.it.vtt 724 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/16. Outro-dps7Ti6Lado.zh-CN.vtt 723 B
    Part 02-Module 03-Lesson 02_Localization Overview/15. Multiple Measurements-gDO4sF8gR9k.ja.vtt 722 B
    Part 02-Module 04-Lesson 01_PID Control/03. Proportional Control - Artificial Intelligence for Robotics-gGo-gSFqYqg.zh-CN.vtt 721 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/27. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.bn.vtt 713 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-DV2cX9W0tT8.zh-CN.vtt 711 B
    Part 03-Module 05-Lesson 01_Autonomous Vehicle Architecture/08. L1 13 L Control Subsystem-ESIz-aSjklY.zh-CN.vtt 710 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/23. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt 709 B
    Part 01-Module 04-Lesson 07_Decision Trees/04. Constructing a Decision Tree First Split-GMe5JT2_oUE.ja.vtt 708 B
    Part 03-Module 03-Lesson 03_Scene Understanding/13. Outro-vyNI5hdMigs.zh-CN.vtt 708 B
    Part 02-Module 02-Lesson 02_Kalman Filters/13. Separated Gaussians-QAqsIWVVX0Y.es-ES.vtt 708 B
    Part 04-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.es-MX.vtt 707 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/23. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt 707 B
    Part 02-Module 04-Lesson 04_Model Predictive Control/03. Dealing With Stopping-2gkRWj7KIMU.zh-CN.vtt 707 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/19. One-Hot Encoding-phYsxqlilUk.en.vtt 707 B
    Part 03-Module 03-Lesson 04_Inference Performance/03. L3 03 L Semantic Segmentation Revisited-xFcI26kLtiY.zh-CN.vtt 706 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-nsSvTTA0p8E.pt-PT.vtt 705 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt 705 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-nsSvTTA0p8E.pt.vtt 702 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-daVA3PI2E6o.zh-CN.vtt 701 B
    Part 02-Module 03-Lesson 05_Particle Filters/09. Moving Robot-_37pf6lV15s.ja.vtt 700 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1--F2gJXWbN6s.pt-PT.vtt 698 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-DV2cX9W0tT8.ja.vtt 697 B
    Part 01-Module 04-Lesson 07_Decision Trees/14. Minimizing Impurity in Splitting-L6J6BRFgDiI.pt-BR.vtt 697 B
    Part 02-Module 04-Lesson 01_PID Control/08. PD Controller Solution - Artificial Intelligence for Robotics-YgomQgfFlTQ.en.vtt 697 B
    Part 01-Module 04-Lesson 07_Decision Trees/04. Constructing a Decision Tree First Split-GMe5JT2_oUE.pt-BR.vtt 696 B
    Part 02-Module 02-Lesson 02_Kalman Filters/10. Predicting the Peak-PsyqM704q2Y.zh-CN.vtt 696 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1--F2gJXWbN6s.pt.vtt 696 B
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/16. L6 24 L Module Outro-QT-4yIV9dEM.en.vtt 695 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-UX3W8TUKbJ0.es-ES.vtt 695 B
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    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-UX3W8TUKbJ0.es.vtt 693 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/05. Rectified Linear Units-2-GirRl7TqU.ja-JP.vtt 693 B
    Part 03-Module 04-Lesson 06_Functional Safety Technical Safety Concept/07. L5 14 L Outro-sIe4SZfDUmM.zh-CN.vtt 692 B
    Part 06-Module 01-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.zh-CN.vtt 692 B
    Part 02-Module 04-Lesson 03_Vehicle Models/14. Dynamic Models - Part 3 Slip Ratio-kSqOJDwRFVc.en.vtt 689 B
    Part 02-Module 03-Lesson 05_Particle Filters/09. Moving Robot-_37pf6lV15s.en.vtt 688 B
    Part 02-Module 02-Lesson 02_Kalman Filters/13. Separated Gaussians-QAqsIWVVX0Y.ja.vtt 688 B
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/01. 04 Sensor Fusion A04 Kalman Filters In C++-Hsvzm7zDG_A.en.vtt 686 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-UX3W8TUKbJ0.pt-PT.vtt 685 B
    Part 02-Module 03-Lesson 02_Localization Overview/05. Uniform Distribution-ysebYA6tDZ4.bn.vtt 684 B
    Part 02-Module 02-Lesson 02_Kalman Filters/06. Evaluate Gaussian-mQtjczyAxQs.es-ES.vtt 684 B
    Part 03-Module 05-Lesson 02_Introduction to ROS/01. 08 System Integration A03 Communication Between Systems-a9hYIvymlkQ.en.vtt 684 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/03. Color-Question-BdQccpMwk80.pt-BR.vtt 683 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-UX3W8TUKbJ0.pt.vtt 682 B
    Part 03-Module 04-Lesson 08_Elective Project Functional Safety/01. L6 25 L Project Outro-k3tl3pkGBa8.en.vtt 680 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/11. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/10. ChainRule-DxOg_olir0k.en.vtt 677 B
    Part 04-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.zh-CN.vtt 675 B
    Part 01-Module 04-Lesson 07_Decision Trees/04. Constructing a Decision Tree First Split-GMe5JT2_oUE.en.vtt 675 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1--F2gJXWbN6s.es-ES.vtt 673 B
    Part 03-Module 02-Lesson 01_Search/09. Maze-ge_-o0RfrgM.zh-CN.vtt 670 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-DLCq1-kOGX0.ar.vtt 669 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/11. Transition to Using Naive Bayes-2_dJXh1qqe0.ar.vtt 669 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/20. Kernel and Gamma-znlTyocTgSc.zh-CN.vtt 664 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1--F2gJXWbN6s.ja.vtt 664 B
    Part 04-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.en.vtt 663 B
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/23. Assignement Predicted Mean And Covariance-0vl_wfDpVec.en.vtt 663 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/08. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt 663 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/24. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt 663 B
    Part 02-Module 03-Lesson 05_Particle Filters/03. Belief Modality-NhKyyhNl70A.ja.vtt 663 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-UX3W8TUKbJ0.en.vtt 659 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/19. One-Hot Encoding-phYsxqlilUk.pt-BR.vtt 657 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-nsSvTTA0p8E.en.vtt 656 B
    Part 02-Module 02-Lesson 02_Kalman Filters/13. Separated Gaussians-QAqsIWVVX0Y.en.vtt 654 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.ru.vtt 653 B
    Part 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels/16. L6 24 L Module Outro-QT-4yIV9dEM.zh-CN.vtt 652 B
    Part 03-Module 05-Lesson 01_Autonomous Vehicle Architecture/05. L1 07 L Components Inputs Wrapup-f3qloIYf16k.en.vtt 652 B
    Part 02-Module 02-Lesson 02_Kalman Filters/06. Evaluate Gaussian-mQtjczyAxQs.en.vtt 651 B
    Part 02-Module 04-Lesson 01_PID Control/08. PD Controller Solution - Artificial Intelligence for Robotics-YgomQgfFlTQ.zh-CN.vtt 647 B
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/20. Jacobian Matrix-pRhuwlMhG3o.en.vtt 646 B
    Part 02-Module 03-Lesson 04_Motion Models/10. Lesson Outro-a_FvfH8OcPg.en.vtt 646 B
    Part 01-Module 04-Lesson 07_Decision Trees/31. Information Gain Calculation Part 10-XYHTuv2FpWQ.ar.vtt 644 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.it.vtt 644 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/18. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt 643 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/01. Welcome to SVM-gnAmmyQ_ZcQ.zh-CN.vtt 643 B
    Part 02-Module 02-Lesson 02_Kalman Filters/21. Kalman Filter Prediciton-SK3cnmu8BYU.es-ES.vtt 643 B
    Part 02-Module 03-Lesson 02_Localization Overview/15. Multiple Measurements-gDO4sF8gR9k.pt-PT.vtt 641 B
    Part 02-Module 03-Lesson 02_Localization Overview/15. Multiple Measurements-gDO4sF8gR9k.pt.vtt 639 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/18. Regularization-Quiz-E0eEW6V0_sA.en-US.vtt 638 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/29. Validation Set Size--2XvoG6WD9k.pt-BR.vtt 638 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.ru.vtt 636 B
    Part 01-Module 01-Lesson 03_Computer Vision Fundamentals/01. 01 Introduction A01 Power Of Cameras-lCPWJEEzUeo.en.vtt 635 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt 634 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/34. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/11. Transition to Using Naive Bayes-2_dJXh1qqe0.ja.vtt 632 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/03. Choosing Between Separating Lines-swoZxkrxIB0.ja.vtt 632 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1--F2gJXWbN6s.it.vtt 631 B
    Part 01-Module 04-Lesson 07_Decision Trees/30. Information Gain Calculation Part 9-V-jzhJoeZj8.ar.vtt 631 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1--F2gJXWbN6s.en.vtt 630 B
    Part 02-Module 04-Lesson 01_PID Control/05. Implement P Controller Solution - Artificial Intelligence for Robotics-wvdFPAOCb64.en.vtt 630 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-nsSvTTA0p8E.zh-CN.vtt 630 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/08. SVM Outlier Practice-WxAO6ByCvew.ja.vtt 628 B
    Part 02-Module 03-Lesson 05_Particle Filters/09. Moving Robot-_37pf6lV15s.zh-CN.vtt 627 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.ja.vtt 627 B
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/02. CTRV-g72HXEcSQHU.zh-CN.vtt 626 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-erdekkpG-Do.zh-CN.vtt 626 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-8cIlVoH5dhw.ja-JP.vtt 624 B
    Part 02-Module 03-Lesson 03_Markov Localization/11. Quiz How Much Data-wzcFHAf-9lo.zh-CN.vtt 624 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/34. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-UX3W8TUKbJ0.zh-CN.vtt 623 B
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    Part 02-Module 02-Lesson 02_Kalman Filters/11. Parameter Update-Lwn6FJgyyYI.es-ES.vtt 622 B
    Part 03-Module 03-Lesson 04_Inference Performance/01. Intro-dK5Yn3sq5RM.en.vtt 622 B
    Part 02-Module 02-Lesson 02_Kalman Filters/06. Evaluate Gaussian-mQtjczyAxQs.ja.vtt 621 B
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/24. Evaluating The Performance-1iVBYQ_KWXk.zh-CN.vtt 620 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.es-MX.vtt 619 B
    Part 02-Module 04-Lesson 04_Model Predictive Control/11. Model Predictive Control Outro-iEdMInAsjgM.en.vtt 619 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.es-ES.vtt 618 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.es.vtt 616 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-DLCq1-kOGX0.ja.vtt 614 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/03. Color-Question-BdQccpMwk80.zh-CN.vtt 612 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-erdekkpG-Do.ja.vtt 611 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.en.vtt 610 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-nsSvTTA0p8E.ja.vtt 610 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/10. ChainRule-DxOg_olir0k.zh-CN.vtt 609 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/07. Quick Fixes #2-It6AEuSDQw0.ar.vtt 608 B
    Part 02-Module 02-Lesson 02_Kalman Filters/04. Variance Comparison-TGdMG81hXc8.es-ES.vtt 608 B
    Part 02-Module 03-Lesson 05_Particle Filters/03. Belief Modality-NhKyyhNl70A.en.vtt 607 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/19. One-Hot Encoding-phYsxqlilUk.zh-CN.vtt 607 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/27. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 B
    Part 02-Module 02-Lesson 02_Kalman Filters/21. Kalman Filter Prediciton-SK3cnmu8BYU.en.vtt 604 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/05. Rectified Linear Units-2-GirRl7TqU.en.vtt 603 B
    Part 02-Module 02-Lesson 02_Kalman Filters/21. Kalman Filter Prediciton-SK3cnmu8BYU.ja.vtt 603 B
    Part 03-Module 02-Lesson 01_Search/09. Maze-yVh0lVlerWs.ru.vtt 603 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.pt-PT.vtt 603 B
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-U2yZxIeG2t0.ar.vtt 602 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.pt.vtt 600 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/34. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 B
    Part 01-Module 03-Lesson 10_Behavioral Cloning Project/16. 12 - More Data Collection-cCZNlX3KLnY.zh-CN.vtt 600 B
    Part 03-Module 02-Lesson 01_Search/06. Compute Cost 2-OXIESpN0KaE.en.vtt 598 B
    Part 01-Module 04-Lesson 07_Decision Trees/27. Information Gain Calculation Part 6-zqmrW9N9WGw.ar.vtt 597 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-erdekkpG-Do.en.vtt 596 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/08. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt 594 B
    Part 02-Module 03-Lesson 04_Motion Models/10. Lesson Outro-a_FvfH8OcPg.zh-CN.vtt 594 B
    Part 02-Module 02-Lesson 02_Kalman Filters/14. Separated Gaussians 2-0FmTokjoRgo.ja.vtt 592 B
    Part 02-Module 03-Lesson 02_Localization Overview/17. Exact Motion-Iky7rJXQU_4.zh-CN.vtt 589 B
    Part 02-Module 04-Lesson 01_PID Control/05. Implement P Controller Solution - Artificial Intelligence for Robotics-wvdFPAOCb64.ja.vtt 589 B
    Part 02-Module 03-Lesson 02_Localization Overview/03. Total Probability-n1EacrqyCs8.pl.vtt 588 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/15. GaussianNB Deployment on Terrain Data-TcSnd3_hAy8.ar.vtt 588 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-sCZI5gWS6mg.zh-CN.vtt 587 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/21. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 B
    Part 01-Module 03-Lesson 10_Behavioral Cloning Project/16. 12 - More Data Collection-cCZNlX3KLnY.en.vtt 583 B
    Part 02-Module 04-Lesson 01_PID Control/05. Implement P Controller Solution - Artificial Intelligence for Robotics-wvdFPAOCb64.zh-CN.vtt 582 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.es-ES.vtt 582 B
    Part 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++/20. Jacobian Matrix-pRhuwlMhG3o.zh-CN.vtt 580 B
    Part 02-Module 04-Lesson 01_PID Control/06. Oscillations - Artificial Intelligence for Robotics-CO3zjkxBaIc.ru.vtt 580 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.es.vtt 579 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/08. SVM Outlier Practice-WxAO6ByCvew.en.vtt 579 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/08. SVM Outlier Practice-WxAO6ByCvew.pt-BR.vtt 577 B
    Part 02-Module 04-Lesson 04_Model Predictive Control/11. Model Predictive Control Outro-iEdMInAsjgM.zh-CN.vtt 576 B
    Part 03-Module 04-Lesson 08_Elective Project Functional Safety/01. L6 25 L Project Outro-k3tl3pkGBa8.zh-CN.vtt 576 B
    Part 02-Module 02-Lesson 02_Kalman Filters/13. Separated Gaussians-QAqsIWVVX0Y.zh-CN.vtt 575 B
    Part 02-Module 03-Lesson 02_Localization Overview/14. Test Sense Function-F8AHaaJVmkw.es-ES.vtt 575 B
    Part 02-Module 03-Lesson 05_Particle Filters/03. Belief Modality-NhKyyhNl70A.zh-CN.vtt 574 B
    Part 02-Module 02-Lesson 02_Kalman Filters/21. Kalman Filter Prediciton-SK3cnmu8BYU.zh-CN.vtt 573 B
    Part 02-Module 03-Lesson 02_Localization Overview/14. Test Sense Function-F8AHaaJVmkw.es.vtt 573 B
    Part 03-Module 05-Lesson 04_Writing ROS Nodes/01. 08 System Integration A05 Closing In-mFoFGT9AtfQ.en.vtt 573 B
    Part 03-Module 03-Lesson 04_Inference Performance/16. Outro-jMSB5_P_fss.en.vtt 572 B
    Part 02-Module 02-Lesson 02_Kalman Filters/06. Evaluate Gaussian-mQtjczyAxQs.zh-CN.vtt 572 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/01. 03 Deep Learning A06 CNNs Have Taken Over-yNOHThuy2UU.en.vtt 572 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.pt-PT.vtt 570 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/03. Choosing Between Separating Lines-swoZxkrxIB0.pt-BR.vtt 568 B
    Part 01-Module 04-Lesson 07_Decision Trees/22. Information Gain Calculation Part 1-erdekkpG-Do.pt-BR.vtt 568 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1--F2gJXWbN6s.zh-CN.vtt 567 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.pt.vtt 567 B
    Part 03-Module 03-Lesson 03_Scene Understanding/01. Intro-z036GNuoiBk.en.vtt 565 B
    Part 02-Module 02-Lesson 06_Unscented Kalman Filters/23. Assignement Predicted Mean And Covariance-0vl_wfDpVec.zh-CN.vtt 565 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/13. Nonlinear Data-PxE2bbG2Hkw.ar.vtt 563 B
    Part 02-Module 02-Lesson 02_Kalman Filters/09. Shifting the Mean-HmcurWkA0fQ.ja.vtt 562 B
    Part 03-Module 02-Lesson 01_Search/06. Compute Cost 2-OXIESpN0KaE.zh-CN.vtt 561 B
    Part 02-Module 02-Lesson 02_Kalman Filters/11. Parameter Update-Lwn6FJgyyYI.en.vtt 561 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-DLCq1-kOGX0.pt-BR.vtt 560 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/18. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt 557 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 B
    Part 02-Module 03-Lesson 05_Particle Filters/03. Belief Modality-5vdbYPc7tWw.en.vtt 556 B
    Part 02-Module 02-Lesson 02_Kalman Filters/12. Parameter Update 2-2BfisMbu86o.es-ES.vtt 556 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/08. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt 555 B
    Part 02-Module 02-Lesson 02_Kalman Filters/14. Separated Gaussians 2-0FmTokjoRgo.es-ES.vtt 554 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/03. Choosing Between Separating Lines-swoZxkrxIB0.zh-CN.vtt 553 B
    Part 01-Module 04-Lesson 07_Decision Trees/04. Constructing a Decision Tree First Split-GMe5JT2_oUE.zh-CN.vtt 552 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/21. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.it.vtt 551 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/12. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/03. Choosing Between Separating Lines-swoZxkrxIB0.en.vtt 549 B
    Part 02-Module 02-Lesson 02_Kalman Filters/04. Variance Comparison-TGdMG81hXc8.en.vtt 549 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/22. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 B
    Part 02-Module 02-Lesson 02_Kalman Filters/09. Shifting the Mean-HmcurWkA0fQ.es-ES.vtt 548 B
    Part 02-Module 03-Lesson 05_Particle Filters/05. Exact or Approximate-WKlm2aO2QGY.ja.vtt 547 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/11. Transition to Using Naive Bayes-2_dJXh1qqe0.pt-BR.vtt 547 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/27. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 B
    Part 02-Module 04-Lesson 03_Vehicle Models/14. Dynamic Models - Part 3 Slip Ratio-kSqOJDwRFVc.zh-CN.vtt 544 B
    Part 02-Module 02-Lesson 02_Kalman Filters/12. Parameter Update 2-2BfisMbu86o.en.vtt 543 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/29. Validation Set Size--2XvoG6WD9k.en-US.vtt 543 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/05. Rectified Linear Units-2-GirRl7TqU.zh-CN.vtt 542 B
    Part 02-Module 03-Lesson 05_Particle Filters/15. Resampling-FjRX_i3SsJA.ja.vtt 542 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 B
    Part 03-Module 02-Lesson 01_Search/06. Compute Cost 2-OXIESpN0KaE.ja.vtt 541 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/29. Validation Set Size--2XvoG6WD9k.ja-JP.vtt 541 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.en.vtt 541 B
    Part 02-Module 02-Lesson 02_Kalman Filters/12. Parameter Update 2-2BfisMbu86o.ja.vtt 540 B
    Part 02-Module 03-Lesson 05_Particle Filters/15. Resampling-FjRX_i3SsJA.en.vtt 540 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/29. Validation Set Size--2XvoG6WD9k.en.vtt 540 B
    Part 02-Module 03-Lesson 05_Particle Filters/03. Belief Modality-5vdbYPc7tWw.zh-CN.vtt 539 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/08. SVM Outlier Practice-WxAO6ByCvew.zh-CN.vtt 539 B
    Part 02-Module 02-Lesson 02_Kalman Filters/09. Shifting the Mean-HmcurWkA0fQ.en.vtt 539 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/06. SVMs and Tricky Data Distributions-wbCq7wm81BU.ar.vtt 538 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/11. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 B
    Part 03-Module 05-Lesson 01_Autonomous Vehicle Architecture/05. L1 07 L Components Inputs Wrapup-f3qloIYf16k.zh-CN.vtt 537 B
    Part 02-Module 03-Lesson 02_Localization Overview/05. Uniform Distribution-ysebYA6tDZ4.ru.vtt 537 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/13. Nonlinear Data-PxE2bbG2Hkw.ja.vtt 535 B
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    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-DLCq1-kOGX0.en.vtt 524 B
    Part 03-Module 03-Lesson 02_Fully Convolutional Networks/12. Outro-ESIl11NfQ7Q.en.vtt 523 B
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    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.ja.vtt 516 B
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    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-qa9B4r5m8wM.zh-CN.vtt 513 B
    Part 02-Module 03-Lesson 05_Particle Filters/10. Add Noise-FQEeI3qzaOM.en.vtt 513 B
    Part 01-Module 01-Lesson 03_Computer Vision Fundamentals/06. region select-ngN9Cr-QfiI.en.vtt 511 B
    Part 03-Module 02-Lesson 01_Search/05. Compute Cost-7-yOaHVeATk.zh-CN.vtt 510 B
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-U2yZxIeG2t0.zh-CN.vtt 508 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/11. Transition to Using Naive Bayes-2_dJXh1qqe0.en.vtt 507 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.es-MX.vtt 506 B
    Part 02-Module 03-Lesson 02_Localization Overview/14. Test Sense Function-F8AHaaJVmkw.pt-PT.vtt 506 B
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    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/22. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 B
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    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-8cIlVoH5dhw.en.vtt 501 B
    Part 02-Module 03-Lesson 02_Localization Overview/11. Sum of Probabilities-z0oijOqN8K8.bn.vtt 500 B
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    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-CIxvkVy1UBI.ar.vtt 496 B
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    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/22. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 B
    Part 03-Module 03-Lesson 04_Inference Performance/16. Outro-jMSB5_P_fss.zh-CN.vtt 494 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-i7pRvuVoWg0.ar.vtt 494 B
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    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-U2yZxIeG2t0.pt-BR.vtt 489 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/15. GaussianNB Deployment on Terrain Data-TcSnd3_hAy8.ja.vtt 487 B
    Part 02-Module 03-Lesson 02_Localization Overview/14. Test Sense Function-F8AHaaJVmkw.zh-CN.vtt 487 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-e21oU80gwWc.en.vtt 486 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/09. From Scatterplots to Decision Surfaces-DLCq1-kOGX0.zh-CN.vtt 485 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-TkaTTptnYdA.pt-BR.vtt 485 B
    Part 02-Module 03-Lesson 05_Particle Filters/05. Exact or Approximate-WKlm2aO2QGY.en.vtt 485 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/13. Nonlinear Data-PxE2bbG2Hkw.zh-CN.vtt 484 B
    Part 03-Module 02-Lesson 01_Search/08. Optimal Path 2-qFswCrEUZSM.ja.vtt 484 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/01. 03 Deep Learning A07 Let'S Go Deeper-SzTpc_EWbDs.en.vtt 484 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/13. Nonlinear Data-PxE2bbG2Hkw.pt-BR.vtt 484 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.pt-PT.vtt 484 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.pt.vtt 481 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/21. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 B
    Part 03-Module 05-Lesson 01_Autonomous Vehicle Architecture/07. L1 12 L Planning Subsystem Connections-5c752eVAR3I.en.vtt 480 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-8cIlVoH5dhw.zh-CN.vtt 479 B
    Part 01-Module 04-Lesson 07_Decision Trees/31. Information Gain Calculation Part 10-XYHTuv2FpWQ.ja.vtt 478 B
    Part 01-Module 04-Lesson 07_Decision Trees/30. Information Gain Calculation Part 9-V-jzhJoeZj8.en.vtt 478 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/02. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt 477 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/12. Participating in open source projects-OxL-gMTizUA.en.vtt 476 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.it.vtt 476 B
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    Part 01-Module 04-Lesson 07_Decision Trees/06. Class Labels After Second Split-A7KKnDmZBA0.ar.vtt 474 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-TkaTTptnYdA.ja-JP.vtt 474 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/11. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt 473 B
    Part 02-Module 02-Lesson 02_Kalman Filters/09. Shifting the Mean-HmcurWkA0fQ.zh-CN.vtt 473 B
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-U2yZxIeG2t0.en.vtt 473 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-e21oU80gwWc.pt.vtt 472 B
    Part 02-Module 03-Lesson 02_Localization Overview/20. Inexact Motion 2-gZbPZLFKS68.ja.vtt 472 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/42. Conclusion-m8xslYUBXYo.en.vtt 471 B
    Part 01-Module 03-Lesson 03_Introduction to TensorFlow/29. Validation Set Size--2XvoG6WD9k.zh-CN.vtt 471 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/13. Nonlinear Data-PxE2bbG2Hkw.en.vtt 470 B
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    Part 02-Module 04-Lesson 01_PID Control/16. Outro-zF_MUxjCl04.en.vtt 470 B
    Part 03-Module 03-Lesson 03_Scene Understanding/01. Intro-z036GNuoiBk.zh-CN.vtt 469 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.it.vtt 469 B
    Part 01-Module 04-Lesson 07_Decision Trees/30. Information Gain Calculation Part 9-V-jzhJoeZj8.ja.vtt 469 B
    Part 02-Module 04-Lesson 01_PID Control/10. Is PD Enough - Artificial Intelligence for Robotics-gDbpwPdStlY.ru.vtt 468 B
    Part 01-Module 04-Lesson 07_Decision Trees/31. Information Gain Calculation Part 10-XYHTuv2FpWQ.en.vtt 468 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.es.vtt 467 B
    Part 01-Module 04-Lesson 07_Decision Trees/18. Entropy Calculation Part 3-M2Sp-Y2a71c.ar.vtt 466 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/06. SVMs and Tricky Data Distributions-wbCq7wm81BU.ja.vtt 466 B
    Part 02-Module 03-Lesson 05_Particle Filters/10. Add Noise-FQEeI3qzaOM.ja.vtt 465 B
    Part 02-Module 02-Lesson 02_Kalman Filters/12. Parameter Update 2-2BfisMbu86o.zh-CN.vtt 463 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.es-ES.vtt 463 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.es.vtt 461 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.ja.vtt 461 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/05. Rectified Linear Units-z9crz_gwGCM.ja-JP.vtt 461 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/15. GaussianNB Deployment on Terrain Data-TcSnd3_hAy8.pt-BR.vtt 461 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt 460 B
    Part 03-Module 02-Lesson 01_Search/08. Optimal Path 2-qFswCrEUZSM.zh-CN.vtt 458 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.en.vtt 457 B
    Part 02-Module 04-Lesson 01_PID Control/16. Outro-zF_MUxjCl04.zh-CN.vtt 457 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt 457 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.es-ES.vtt 456 B
    Part 02-Module 04-Lesson 01_PID Control/06. Oscillations - Artificial Intelligence for Robotics-CO3zjkxBaIc.ja.vtt 455 B
    Part 02-Module 02-Lesson 02_Kalman Filters/11. Parameter Update-Lwn6FJgyyYI.zh-CN.vtt 455 B
    README.txt 454 B
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    Part 05-Module 02-Lesson 01_GitHub Profile Review/07. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt 453 B
    Part 02-Module 02-Lesson 02_Kalman Filters/14. Separated Gaussians 2-0FmTokjoRgo.zh-CN.vtt 452 B
    Part 03-Module 03-Lesson 02_Fully Convolutional Networks/12. Outro-ESIl11NfQ7Q.zh-CN.vtt 451 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-TkaTTptnYdA.en.vtt 451 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/15. GaussianNB Deployment on Terrain Data-TcSnd3_hAy8.en.vtt 449 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.ja.vtt 448 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.en.vtt 448 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.pt-PT.vtt 446 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.pt.vtt 443 B
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-U2yZxIeG2t0.ja.vtt 443 B
    Part 02-Module 03-Lesson 05_Particle Filters/18. Never Sampled 3-Z1oQl-1cUeE.en.vtt 441 B
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/07. Nd113 C6 L3 055 L Moving At 53 Degrees Solution V2-Y_3M6eeYbd8.zh-CN.vtt 440 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/12. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/10. A Good Linear Decision Surface-sudTOiG-NJo.ar.vtt 438 B
    Part 02-Module 03-Lesson 05_Particle Filters/10. Add Noise-FQEeI3qzaOM.zh-CN.vtt 437 B
    Part 01-Module 04-Lesson 07_Decision Trees/27. Information Gain Calculation Part 6-zqmrW9N9WGw.pt-BR.vtt 436 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.it.vtt 436 B
    Part 03-Module 02-Lesson 01_Search/08. Optimal Path 2-GvCSZOR3hLQ.ru.vtt 435 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/07. Quick Fixes #2-It6AEuSDQw0.en.vtt 435 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/06. SVMs and Tricky Data Distributions-wbCq7wm81BU.en.vtt 435 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/42. Conclusion-m8xslYUBXYo.zh-CN.vtt 434 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-i7pRvuVoWg0.pt-BR.vtt 433 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/03. Number of Parameters-TkaTTptnYdA.zh-CN.vtt 433 B
    Part 01-Module 01-Lesson 03_Computer Vision Fundamentals/06. region select-ngN9Cr-QfiI.zh-CN.vtt 432 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-i7pRvuVoWg0.ja.vtt 431 B
    Part 02-Module 03-Lesson 05_Particle Filters/03. Belief Modality-5vdbYPc7tWw.ja.vtt 430 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.en.vtt 430 B
    Part 02-Module 03-Lesson 05_Particle Filters/21. Orientation 1-cupiUHaKvdI.ja.vtt 423 B
    Part 03-Module 02-Lesson 01_Search/09. Maze-yVh0lVlerWs.zh-CN.vtt 423 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/08. From Scatterplots to Predictions 2-vG3ahYyLHlQ.ar.vtt 423 B
    Part 01-Module 04-Lesson 07_Decision Trees/06. Class Labels After Second Split--3VPMBIwTtE.ar.vtt 422 B
    Part 01-Module 04-Lesson 07_Decision Trees/27. Information Gain Calculation Part 6-zqmrW9N9WGw.en.vtt 422 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.ja.vtt 421 B
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    Part 01-Module 04-Lesson 07_Decision Trees/31. Information Gain Calculation Part 10-XYHTuv2FpWQ.zh-CN.vtt 420 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/16. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 B
    Part 02-Module 03-Lesson 02_Localization Overview/20. Inexact Motion 2-gZbPZLFKS68.es-ES.vtt 420 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/09. Handoff to Katie-GkqOdgZnkig.ar.vtt 419 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-U3FUxkm1MxI.en.vtt 419 B
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-CIxvkVy1UBI.pt-BR.vtt 417 B
    Part 01-Module 04-Lesson 07_Decision Trees/28. Information Gain Calculation Part 7-EDFp4wU5BMo.pt-BR.vtt 417 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-wOfAyDvun5w.zh-CN.vtt 416 B
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-CIxvkVy1UBI.ja.vtt 416 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/15. GaussianNB Deployment on Terrain Data-TcSnd3_hAy8.zh-CN.vtt 416 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-nsSvTTA0p8E.es-ES.vtt 416 B
    Part 02-Module 03-Lesson 05_Particle Filters/21. Orientation 1-cupiUHaKvdI.zh-CN.vtt 415 B
    Part 02-Module 03-Lesson 02_Localization Overview/09. Normalize Distribution-Uc_rHR6U70U.zh-CN.vtt 414 B
    Part 01-Module 04-Lesson 07_Decision Trees/19. Entropy Calculation Part 4-V0FNwMKhIVM.ar.vtt 413 B
    Part 02-Module 03-Lesson 02_Localization Overview/06. Generalized Uniform Distribution-nsSvTTA0p8E.es.vtt 413 B
    Part 03-Module 02-Lesson 01_Search/09. Maze-yVh0lVlerWs.en.vtt 412 B
    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.bn.vtt 411 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/07. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt 410 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-i7pRvuVoWg0.zh-CN.vtt 409 B
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    Part 02-Module 02-Lesson 02_Kalman Filters/07. Maximize Gaussian-2cD8T65E-jM.es-ES.vtt 408 B
    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.ru.vtt 406 B
    Part 02-Module 03-Lesson 02_Localization Overview/10. pHit and pMiss-FnhHQht4vDo.zh-CN.vtt 406 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/06. SVMs and Tricky Data Distributions-wbCq7wm81BU.pt-BR.vtt 405 B
    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-CIxvkVy1UBI.en.vtt 405 B
    Part 01-Module 04-Lesson 07_Decision Trees/09. [object Object]-i7pRvuVoWg0.en.vtt 404 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/06. SVMs and Tricky Data Distributions-wbCq7wm81BU.zh-CN.vtt 401 B
    Part 02-Module 03-Lesson 05_Particle Filters/17. Never Sampled 2-q95KMAIqDDY.en.vtt 401 B
    Part 02-Module 03-Lesson 02_Localization Overview/20. Inexact Motion 2-gZbPZLFKS68.zh-CN.vtt 400 B
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    Part 02-Module 03-Lesson 02_Localization Overview/11. Sum of Probabilities-6c0XvswnGm0.bn.vtt 400 B
    Part 02-Module 03-Lesson 05_Particle Filters/16. Never Sampled 1-8ffPkDiDioI.ja.vtt 398 B
    Part 02-Module 03-Lesson 02_Localization Overview/05. Uniform Distribution-ysebYA6tDZ4.ja.vtt 398 B
    Part 01-Module 04-Lesson 07_Decision Trees/27. Information Gain Calculation Part 6-zqmrW9N9WGw.ja.vtt 398 B
    Part 02-Module 03-Lesson 05_Particle Filters/21. Orientation 1-cupiUHaKvdI.en.vtt 397 B
    Part 02-Module 02-Lesson 02_Kalman Filters/07. Maximize Gaussian-2cD8T65E-jM.en.vtt 397 B
    Part 02-Module 03-Lesson 02_Localization Overview/05. Uniform Distribution-ysebYA6tDZ4.es-ES.vtt 395 B
    Part 02-Module 03-Lesson 02_Localization Overview/20. Inexact Motion 2-gZbPZLFKS68.it.vtt 394 B
    Part 03-Module 05-Lesson 01_Autonomous Vehicle Architecture/07. L1 12 L Planning Subsystem Connections-5c752eVAR3I.zh-CN.vtt 393 B
    Part 05-Module 02-Lesson 01_GitHub Profile Review/15. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 B
    Part 02-Module 03-Lesson 02_Localization Overview/05. Uniform Distribution-ysebYA6tDZ4.es.vtt 392 B
    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/11. Nd113 C6 L3 095 L Trigonometric Ratios Solution V2-c5iuVhWCOzc.en.vtt 391 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/05. Rectified Linear Units-z9crz_gwGCM.en.vtt 390 B
    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/16. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 B
    Part 01-Module 04-Lesson 07_Decision Trees/28. Information Gain Calculation Part 7-EDFp4wU5BMo.ja.vtt 389 B
    Part 01-Module 04-Lesson 07_Decision Trees/29. Information Gain Calculation Part 8-c7UjSq7Fmr8.ar.vtt 388 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.pt-PT.vtt 387 B
    Part 02-Module 03-Lesson 02_Localization Overview/20. Inexact Motion 2-gZbPZLFKS68.pt-PT.vtt 384 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.pt.vtt 384 B
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    Part 02-Module 04-Lesson 01_PID Control/06. Oscillations - Artificial Intelligence for Robotics-CO3zjkxBaIc.en.vtt 378 B
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    Part 01-Module 04-Lesson 07_Decision Trees/05. Constructing a Decision Tree 2nd Split-CIxvkVy1UBI.zh-CN.vtt 371 B
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    Part 01-Module 03-Lesson 01_Introduction to Neural Networks/16. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 B
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    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/10. A Good Linear Decision Surface-sudTOiG-NJo.pt-BR.vtt 363 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/05. Practice with Margins-l3zXhTxQiTs.ar.vtt 362 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.es-ES.vtt 362 B
    Part 02-Module 02-Lesson 02_Kalman Filters/22. Another Prediction-cUKlYjQEQGY.es-ES.vtt 362 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.it.vtt 361 B
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    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.es.vtt 359 B
    Part 01-Module 04-Lesson 07_Decision Trees/07. Constructing A Decision TreeThird Split-RxySNoOmXnc.ar.vtt 359 B
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    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/10. A Good Linear Decision Surface-sudTOiG-NJo.ja.vtt 357 B
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    Part 01-Module 04-Lesson 07_Decision Trees/12. Decision Tree Accuracy-1z5mVNdF1KA.ja.vtt 357 B
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    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.ja.vtt 356 B
    Part 01-Module 04-Lesson 07_Decision Trees/12. Decision Tree Accuracy-1z5mVNdF1KA.ar.vtt 356 B
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    Part 02-Module 03-Lesson 02_Localization Overview/22. Inexact Move Function-68Kao9dkIKA.es-ES.vtt 351 B
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    Part 01-Module 04-Lesson 07_Decision Trees/18. Entropy Calculation Part 3-M2Sp-Y2a71c.ja.vtt 350 B
    Part 02-Module 02-Lesson 02_Kalman Filters/07. Maximize Gaussian-2cD8T65E-jM.zh-CN.vtt 349 B
    Part 01-Module 03-Lesson 04_Deep Neural Networks/05. Rectified Linear Units-z9crz_gwGCM.zh-CN.vtt 348 B
    Part 02-Module 03-Lesson 02_Localization Overview/13. Normalized Sense Function-GqWszyHTYas.en.vtt 348 B
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    Part 03-Module 02-Lesson 01_Search/18. Computing Value-ebFQqd7Uhek.ru.vtt 341 B
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    Part 02-Module 04-Lesson 01_PID Control/06. Oscillations - Artificial Intelligence for Robotics-CO3zjkxBaIc.zh-CN.vtt 340 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-AMFig-sYGfM.ja.vtt 340 B
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    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/10. A Good Linear Decision Surface-sudTOiG-NJo.en.vtt 337 B
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    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-TF6AWXSlOcY.pt.vtt 330 B
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    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/02. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt 324 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/08. From Scatterplots to Predictions 2-vG3ahYyLHlQ.pt-BR.vtt 324 B
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    Part 01-Module 01-Lesson 03_Computer Vision Fundamentals/14. Hough Quiz 5-upKjISd3aBk.zh-CN.vtt 322 B
    Part 02-Module 03-Lesson 02_Localization Overview/25. Move 1000-nYt9b_pNvEE.ja.vtt 321 B
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    Part 02-Module 02-Lesson 02_Kalman Filters/05. Preferred Gaussian-sBsju-6nQWI.zh-CN.vtt 315 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-AMFig-sYGfM.es-ES.vtt 314 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-oDPbdGXH5nE.ja.vtt 314 B
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    Part 01-Module 04-Lesson 07_Decision Trees/19. Entropy Calculation Part 4-V0FNwMKhIVM.ja.vtt 314 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/08. From Scatterplots to Predictions 2-tkllhaHoko8.ja.vtt 314 B
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    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.pt.vtt 311 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/10. A Good Linear Decision Surface-sudTOiG-NJo.zh-CN.vtt 311 B
    Part 02-Module 03-Lesson 02_Localization Overview/24. Move Twice-oqlgQa1IdcY.es-ES.vtt 310 B
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    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/02. Intro to CNNs-B61jxZ4rkMs.en-US.vtt 309 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-TF6AWXSlOcY.es-ES.vtt 309 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/09. Handoff to Katie-GkqOdgZnkig.pt-BR.vtt 307 B
    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.es-ES.vtt 306 B
    Part 03-Module 02-Lesson 01_Search/19. Computing Value 2-t2aT92C2ruA.ja.vtt 305 B
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    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.es.vtt 303 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/02. Intro to CNNs-B61jxZ4rkMs.en.vtt 303 B
    Part 03-Module 02-Lesson 01_Search/18. Computing Value-ebFQqd7Uhek.ja.vtt 303 B
    Part 02-Module 04-Lesson 01_PID Control/10. Is PD Enough - Artificial Intelligence for Robotics-gDbpwPdStlY.zh-CN.vtt 302 B
    Part 01-Module 03-Lesson 05_Convolutional Neural Networks/02. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt 301 B
    Part 02-Module 02-Lesson 02_Kalman Filters/22. Another Prediction-cUKlYjQEQGY.ja.vtt 301 B
    Part 02-Module 02-Lesson 02_Kalman Filters/06. Evaluate Gaussian-4-0nBfsD4jo.es-ES.vtt 301 B
    Part 02-Module 03-Lesson 02_Localization Overview/19. Inexact Motion 1-mGWGhgZG_jM.en.vtt 301 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-oDPbdGXH5nE.en.vtt 299 B
    Part 02-Module 04-Lesson 02_PID Controller Project/01. 07 Control A01 C++ On The Vehicle-LjlgqozwzzA.en.vtt 299 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-TF6AWXSlOcY.it.vtt 298 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-oDPbdGXH5nE.it.vtt 298 B
    Part 02-Module 02-Lesson 02_Kalman Filters/05. Preferred Gaussian-sBsju-6nQWI.en.vtt 298 B
    Part 01-Module 04-Lesson 07_Decision Trees/23. Information Gain Calculation Part 2-t4qaavAslSw.ja.vtt 297 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-AMFig-sYGfM.en.vtt 297 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/05. Practice with Margins-l3zXhTxQiTs.pt-BR.vtt 297 B
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    Part 01-Module 04-Lesson 07_Decision Trees/29. Information Gain Calculation Part 8-c7UjSq7Fmr8.ja.vtt 297 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/08. From Scatterplots to Predictions 2-vG3ahYyLHlQ.ja.vtt 296 B
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    Part 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher/11. Nd113 C6 L3 095 L Trigonometric Ratios Solution V2-c5iuVhWCOzc.zh-CN.vtt 296 B
    Part 02-Module 03-Lesson 02_Localization Overview/24. Move Twice-oqlgQa1IdcY.it.vtt 295 B
    Part 02-Module 02-Lesson 02_Kalman Filters/13. Separated Gaussians-fGcozDEwnwY.es-ES.vtt 294 B
    Part 01-Module 01-Lesson 03_Computer Vision Fundamentals/14. Hough Quiz 5-upKjISd3aBk.en.vtt 294 B
    Part 02-Module 03-Lesson 02_Localization Overview/19. Inexact Motion 1-mGWGhgZG_jM.zh-CN.vtt 294 B
    Part 02-Module 03-Lesson 02_Localization Overview/19. Inexact Motion 1-mGWGhgZG_jM.pt.vtt 293 B
    Part 01-Module 04-Lesson 07_Decision Trees/06. Class Labels After Second Split-A7KKnDmZBA0.pt-BR.vtt 293 B
    Part 03-Module 02-Lesson 01_Search/10. Maze 2-aBUxPyEDOWw.ru.vtt 292 B
    Part 02-Module 02-Lesson 02_Kalman Filters/06. Evaluate Gaussian-4-0nBfsD4jo.en.vtt 292 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/08. From Scatterplots to Predictions 2-vG3ahYyLHlQ.zh-CN.vtt 292 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/08. From Scatterplots to Predictions 2-tkllhaHoko8.pt-BR.vtt 291 B
    Part 02-Module 03-Lesson 02_Localization Overview/24. Move Twice-oqlgQa1IdcY.en.vtt 290 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-oDPbdGXH5nE.pt-PT.vtt 290 B
    Part 01-Module 04-Lesson 07_Decision Trees/19. Entropy Calculation Part 4-V0FNwMKhIVM.en.vtt 290 B
    Part 02-Module 03-Lesson 02_Localization Overview/25. Move 1000-nYt9b_pNvEE.it.vtt 290 B
    Part 01-Module 04-Lesson 07_Decision Trees/29. Information Gain Calculation Part 8-c7UjSq7Fmr8.en.vtt 289 B
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    Part 02-Module 03-Lesson 02_Localization Overview/25. Move 1000-nYt9b_pNvEE.en.vtt 288 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-oDPbdGXH5nE.pt.vtt 287 B
    Part 02-Module 03-Lesson 02_Localization Overview/24. Move Twice-oqlgQa1IdcY.pt-PT.vtt 287 B
    Part 01-Module 04-Lesson 07_Decision Trees/12. Decision Tree Accuracy-1z5mVNdF1KA.zh-CN.vtt 287 B
    Part 02-Module 03-Lesson 02_Localization Overview/25. Move 1000-nYt9b_pNvEE.zh-CN.vtt 286 B
    Part 01-Module 04-Lesson 07_Decision Trees/12. Decision Tree Accuracy-1z5mVNdF1KA.en.vtt 286 B
    Part 03-Module 05-Lesson 01_Autonomous Vehicle Architecture/09. L1 15 L On To The Code-_jhipYTqp3U.zh-CN.vtt 286 B
    Part 01-Module 04-Lesson 07_Decision Trees/06. Class Labels After Second Split-A7KKnDmZBA0.en.vtt 285 B
    Part 01-Module 04-Lesson 07_Decision Trees/30. Information Gain Calculation Part 9-PDqyWzZCVBY.ar.vtt 285 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/08. From Scatterplots to Predictions 2-tkllhaHoko8.zh-CN.vtt 285 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/09. Handoff to Katie-GkqOdgZnkig.en.vtt 285 B
    Part 02-Module 03-Lesson 02_Localization Overview/11. Sum of Probabilities-z0oijOqN8K8.it.vtt 284 B
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    Part 02-Module 03-Lesson 02_Localization Overview/25. Move 1000-nYt9b_pNvEE.pt-PT.vtt 284 B
    Part 01-Module 04-Lesson 07_Decision Trees/24. Information Gain Calculation Part 3-yWPbe8onCeA.ar.vtt 283 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/09. Handoff to Katie-GkqOdgZnkig.zh-CN.vtt 282 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-TF6AWXSlOcY.en.vtt 282 B
    Part 02-Module 03-Lesson 05_Particle Filters/02. State Space-oyw7WEHMvVY.zh-CN.vtt 282 B
    Part 01-Module 04-Lesson 07_Decision Trees/24. Information Gain Calculation Part 3-s_-I8mbrfw0.ja.vtt 281 B
    Part 02-Module 02-Lesson 02_Kalman Filters/22. Another Prediction-cUKlYjQEQGY.zh-CN.vtt 281 B
    Part 02-Module 03-Lesson 02_Localization Overview/25. Move 1000-nYt9b_pNvEE.pt.vtt 281 B
    Part 02-Module 02-Lesson 02_Kalman Filters/17. Predict Function-AMFig-sYGfM.zh-CN.vtt 280 B
    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.en.vtt 280 B
    Part 02-Module 03-Lesson 02_Localization Overview/07. Probability After Sense-dEiQObhi2J4.ja.vtt 280 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/16. Separating with the New Feature-9KAHkienFWk.ja.vtt 279 B
    Part 01-Module 04-Lesson 07_Decision Trees/25. Information Gain Calculation Part 4-j0uDMc3Yrlo.ar.vtt 278 B
    Part 02-Module 03-Lesson 02_Localization Overview/31. Formal Definition of Probability 3-oDPbdGXH5nE.zh-CN.vtt 277 B
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    Part 01-Module 04-Lesson 07_Decision Trees/29. Information Gain Calculation Part 8-c7UjSq7Fmr8.pt-BR.vtt 276 B
    Part 03-Module 02-Lesson 01_Search/18. Computing Value-ebFQqd7Uhek.en.vtt 275 B
    Part 02-Module 02-Lesson 02_Kalman Filters/16. Gaussian Motion-xNPEjY4dsds.es-ES.vtt 275 B
    Part 02-Module 03-Lesson 05_Particle Filters/02. State Space-oyw7WEHMvVY.en.vtt 274 B
    Part 01-Module 04-Lesson 07_Decision Trees/19. Entropy Calculation Part 4-V0FNwMKhIVM.pt-BR.vtt 273 B
    Part 01-Module 04-Lesson 06_Support Vector Machines/05. Practice with Margins-l3zXhTxQiTs.en.vtt 273 B
    Part 01-Module 04-Lesson 07_Decision Trees/19. Entropy Calculation Part 4-bhwb2v9rEdI.pt-BR.vtt 272 B
    Part 02-Module 02-Lesson 02_Kalman Filters/13. Separated Gaussians-fGcozDEwnwY.ja.vtt 271 B
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    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-IZC33Tmy8Lo.en.vtt 150 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/04. Speed Scatterplot Grade and Bumpiness-v2UbL0SOm9A.ja.vtt 149 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1-OQ2JS2wQzrs.es-ES.vtt 147 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1-OQ2JS2wQzrs.en.vtt 142 B
    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-IZC33Tmy8Lo.it.vtt 138 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/04. Speed Scatterplot Grade and Bumpiness-v2UbL0SOm9A.ar.vtt 136 B
    Part 02-Module 03-Lesson 02_Localization Overview/29. Formal Definition of Probability 1-OQ2JS2wQzrs.ja.vtt 135 B
    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-IZC33Tmy8Lo.ja.vtt 133 B
    Part 01-Module 04-Lesson 07_Decision Trees/29. Information Gain Calculation Part 8-F-xSYJ3y_pA.ja.vtt 131 B
    Part 01-Module 04-Lesson 07_Decision Trees/29. Information Gain Calculation Part 8-F-xSYJ3y_pA.en.vtt 131 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/04. Speed Scatterplot Grade and Bumpiness-v2UbL0SOm9A.pt-BR.vtt 130 B
    Part 03-Module 02-Lesson 01_Search/19. Computing Value 2-yTV3JPJk1kE.ru.vtt 129 B
    Part 01-Module 04-Lesson 05_Machine Learning and Stanley/04. Speed Scatterplot Grade and Bumpiness-v2UbL0SOm9A.en.vtt 128 B
    Part 02-Module 03-Lesson 02_Localization Overview/04. Uniform Probability Quiz-IZC33Tmy8Lo.zh-CN.vtt 128 B
    Part 01-Module 04-Lesson 07_Decision Trees/26. Information Gain Calculation Part 5-4oOXVejgFGk.ar.vtt 126 B
    Part 03-Module 02-Lesson 01_Search/19. Computing Value 2-yTV3JPJk1kE.ja.vtt 125 B
    Part 01-Module 04-Lesson 07_Decision Trees/28. Information Gain Calculation Part 7-frzL4n6Y-vU.ar.vtt 123 B
    Part 01-Module 04-Lesson 07_Decision Trees/29. Information Gain Calculation Part 8-F-xSYJ3y_pA.pt-BR.vtt 122 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.es-ES.vtt 120 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.ja.vtt 119 B
    Part 01-Module 04-Lesson 07_Decision Trees/28. Information Gain Calculation Part 7-frzL4n6Y-vU.ja.vtt 119 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.es.vtt 117 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.zh-CN.vtt 115 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.en.vtt 113 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.it.vtt 111 B
    Part 01-Module 04-Lesson 07_Decision Trees/28. Information Gain Calculation Part 7-frzL4n6Y-vU.pt-BR.vtt 111 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.pt-PT.vtt 110 B
    Part 03-Module 02-Lesson 01_Search/19. Computing Value 2-yTV3JPJk1kE.en.vtt 109 B
    Part 01-Module 04-Lesson 07_Decision Trees/28. Information Gain Calculation Part 7-frzL4n6Y-vU.en.vtt 108 B
    Part 02-Module 03-Lesson 02_Localization Overview/08. Compute Sum-WgX17_mmc1c.pt.vtt 107 B
    Part 01-Module 04-Lesson 07_Decision Trees/26. Information Gain Calculation Part 5-4oOXVejgFGk.ja.vtt 107 B
    Part 02-Module 03-Lesson 02_Localization Overview/30. Formal Definition of Probability 2-uw51WQDqXAI.es-ES.vtt 105 B
    Part 03-Module 02-Lesson 01_Search/19. Computing Value 2-yTV3JPJk1kE.zh-CN.vtt 104 B
    Part 02-Module 03-Lesson 02_Localization Overview/30. Formal Definition of Probability 2-uw51WQDqXAI.ja.vtt 102 B
    Part 01-Module 04-Lesson 07_Decision Trees/26. Information Gain Calculation Part 5-4oOXVejgFGk.en.vtt 101 B
    Part 01-Module 04-Lesson 07_Decision Trees/16. Entropy Calculation Part 1-JX3NN5zwL08.ar.vtt 99 B
    Part 02-Module 03-Lesson 02_Localization Overview/30. Formal Definition of Probability 2-uw51WQDqXAI.pt-PT.vtt 98 B
    Part 02-Module 03-Lesson 02_Localization Overview/30. Formal Definition of Probability 2-uw51WQDqXAI.en.vtt 96 B
    Part 02-Module 03-Lesson 02_Localization Overview/30. Formal Definition of Probability 2-uw51WQDqXAI.pt.vtt 95 B
    Part 02-Module 03-Lesson 02_Localization Overview/30. Formal Definition of Probability 2-uw51WQDqXAI.it.vtt 93 B
    Part 01-Module 03-Lesson 02_MiniFlow/08. Pixels are Features!-qE5YYXtPq9U.pt-BR.vtt 91 B
    Part 02-Module 03-Lesson 02_Localization Overview/30. Formal Definition of Probability 2-uw51WQDqXAI.zh-CN.vtt 91 B
    Part 01-Module 04-Lesson 07_Decision Trees/16. Entropy Calculation Part 1-JX3NN5zwL08.pt-BR.vtt 90 B
    Part 01-Module 04-Lesson 07_Decision Trees/16. Entropy Calculation Part 1-JX3NN5zwL08.ja.vtt 89 B
    Part 01-Module 04-Lesson 07_Decision Trees/16. Entropy Calculation Part 1-JX3NN5zwL08.en.vtt 86 B
    Part 01-Module 04-Lesson 03_Advanced Techniques for Lane Finding/03. Finding Lane Pixels by Histogram and Sliding Window-siAMDK8C_x8.zh-CN.vtt 79 B
    Part 01-Module 03-Lesson 02_MiniFlow/08. Pixels are Features!-qE5YYXtPq9U.en-US.vtt 72 B

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