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Self Car Driving Engineer
Important folder
Download this folder before begin
1.Computer Vision Fundamentals
Meet The Mercedes Team (3:47)
Welcome 1 (0:26)
overview 1 (0:39)
02. Setting up the Problem (0:55)
03. Color Selection (0:44)
04. Color Selection Code Example
05. Color Selection
06. Region Masking (0:18)
07. Color and Region Combined
08. Color Region
09. Finding Lines of Any Color
10. What is Computer Vision? (0:38)
11. Canny Edge Detection (1:21)
012. Canny to Detect Lane Lines
13. Canny Edges
14. Hough Transform (2:22)
15. Hough Transform to Find Lane Lines
16. Hough Transform
2.Finding Lane Lines Project
Project Expectations
04. Project instructions Workspaces
05. Finding Lane Lines
06. Project instructions Local Setup
07.Starter Kit Installation
08. Run Some Code!
Introduction to Neural Networks
04. A Note on Deep Learning
05. Quiz: Housing Prices (1:04)
06. Solution: Housing Prices (0:37)
07. Linear to Logistic Regression
08. Classification Problems 1 (1:38)
09. Classification Problems 2 (1:03)
10. Linear Boundaries (2:43)
11. Higher Dimensions (2:01)
11. Why "Neural Networks"?
12. Perceptrons as Logical Operators (6:00)
13. Perceptrons II
XOR Perceptron (0:35)
Perceptron Trick (4:48)
17. Perceptron Algorithm (2:14)
18. Non-Linear Regions (1:00)
20. Log-loss Error Function (5:50)
21. Discrete vs Continuous (3:58)
22. Softmax (5:07)
23. One-Hot Encoding (1:19)
24. Maximum Likelihood (3:08)
25. Maximizing Probabilities (1:33)
26. Cross-Entropy 1 (3:17)
27. Cross-Entropy 2 (5:32)
28. Multi-Class Cross Entropy (3:11)
29. Logistic Regression (4:53)
30. Gradient Descent (2:51)
31. Gradient Descent: The Code
32. Perceptron vs Gradient Descent (2:37)
33. Continuous Perceptrons (0:54)
35. Non-Linear Models (0:52)
36. Neural Network Architecture (9:23)
37. Feedforward (5:39)
38. Multilayer Perceptrons (1:00)
39. Backpropagation (13:23)
MiniFlow
02. Introduction
03. Graphs (0:03)
04. MiniFlow Architecture
05. Forward Propagation
06. Forward Propagation Solution
07. Learning and Loss
08. Linear Transform (0:10)
09. Sigmoid Function
10. Cost
11. Gradient Descent Part 1
12. Gradient Descent Part 2
13. Backpropagation
14. Stochastic Gradient Descent
15. SGD Solution
16. Under the Hood Part 1
Introduction to TensorFlow
06. Installing TensorFlow
07. Hello, Tensor World!
08.Tensorflow Input
09. Tensorflow Math
10. Transition to Classification
11. Supervised Classification (0:47)
13. Training Your Logistic Classifier (1:46)
14. TensorFlow Linear Function
16. Linear Update
19. One-Hot Encoding (0:26)
21. Cross Entropy (1:39)
22. Minimizing Cross Entropy (1:54)
24. Numerical Stability (0:29)
25. Normalized Inputs and Initial Weights (2:46)
26. Measuring Performance (3:33)
28. Validation and Test Set Size (0:41)
29. Validation Set Size (1:06)
30. Validation Test Set Size Continued (0:42)
32. Stochastic Gradient Descent (2:25)
33. Momentum and Learning Rate Decay (1:30)
34. Parameter Hyperspace! (1:29)
37. Epochs
38. AWS GPU Instances
Deep Neural Networks
04. Linear Models are Limited (1:26)
03. Number of Parameters (0:42)
05. Rectified Linear Units (0:41)
06. Network of ReLUs (0:45)
07. 2-Layer Neural Network
10. The Chain Rule (0:23)
11. Backprop (1:54)
12. Deep Neural Network in TensorFlow
13. Training a Deep Learning Network (1:15)
14. Save and Restore TensorFlow Models
15. Finetuning
17. Regularization (0:59)
19. Dropout (1:43)
20. Dropout Pt. 2 (0:48)
TensorFlow Dropout
Convolutional Neural Networks
03. Color (0:30)
04. Statistical Invariance (1:32)
05. Convolutional Networks (3:04)
06. Intuition
07. Filters
08. Feature Map Sizes (0:27)
10. Parameters
09. Convolutions continued (0:37)
17. Visualizing CNNs
18. TensorFlow Convolution Layer
19. Explore The Design Space (2:16)
20. TensorFlow Max Pooling
29. 1x1 Convolutions (1:10)
30. Inception Module (1:06)
31. Convolutional Network in TensorFlow
32. TensorFlow Convolution Layer
33. Solution: TensorFlow Convolution Layer
34. TensorFlow Pooling Layer
35. Solution: TensorFlow Pooling Layer
36. Lab: LeNet in TensorFlow
38. CNNs - Additional Resources
LeNet for Traffic Signs
01. LeNet Architecture (1:02)
03. LeNet Data (1:37)
04. LeNet Implementation (3:00)
05. LeNet Training Pipeline (1:52)
06. LeNet Evaluation Pipeline (0:53)
07. LeNet Training the Model (1:04)
08. LeNet Testing (0:36)
09. LeNet on AWS (3:08)
10. LeNet for Traffic Signs (3:41)
11. Visualizing Layers
Traffic Sign Classifier Project
03. GPU Options for the Project
05. Project Instructions
06. Traffic Sign Classifier
Traffic Sign Classifier
Keras
Keras Overview
06. Neural Networks in Keras
07. Convolutions in Keras
08. Pooling in Keras
09. Dropout in Keras
10. Testing in Keras
Behavioral Cloning Project
03. Project Resources
04. Running the Simulator (0:56)
05. Data Collection Tactics (1:01)
06. Data Collection Strategies
07. Data Visualization (2:06)
08. Training the Network (3:12)
09. Running Your Network (1:03)
10. Data Preprocessing (1:11)
11. More Networks (0:45)
12. Data Augmentation (1:15)
13. Using Multiple Cameras (1:45)
14. Cropping Images in Keras (0:47)
15. Even More Powerful Network (0:51)
17. Visualizing Loss
18. Generators
19. Recording Video in Autonomous Mode
20. Project Workspace Instructions
Camera Calibration
05. Distortion Correction (0:56)
07. Pinhole Camera Model (2:50)
10. Finding Corners
11. Calibrating Your Camera (4:43)
12. Correcting for Distortion
14. Perspective Transform (2:48)
16. Transform a Stop Sign (4:31)
18. Undistort and Transform
Gradients and Color Spaces
02. Sobel Operator
03. Applying Sobel
04. Magnitude of the Gradient
05. Direction of the Gradient
06. Combining Thresholds
08. Color Thresholding (3:10)
10. HLS and Color Thresholds
12. Color and Gradient
Advanced Techniques for Lane Finding
02. Processing Each Image
03. Finding the Lines: Histogram Peaks (0:25)
04. Finding the Lines: Sliding Window
05. Finding the Lines: Search from Prior
06. Another Sliding Window Search
07. Measuring Curvature I
08. Measuring Curvature II
Advanced Lane Finding Project
03. Tips and Tricks for the Project
04. Project Instructions
Machine Learning and Stanley
04. Speed Scatterplot: Grade and Bumpiness (1:31)
05. Speed Scatterplot 2 (0:13)
06. Speed Scatterplot 3 (0:40)
07. From Scatterplots to Predictions (0:40)
08. From Scatterplots to Predictions 2 (0:19)
09. From Scatterplots to Decision Surfaces (0:52)
10. A Good Linear Decision Surface (1:03)
11. Transition to Using Naive Bayes (0:17)
12. NB Decision Boundary in Python (0:37)
13. Getting Started With sklearn (1:25)
14. Gaussian NB Example (3:02)
15. GaussianNB Deployment on Terrain Data (1:08)
16. Calculating NB Accuracy (2:07)
Support Vector Machines
02. Separating Line (0:56)
03. Choosing Between Separating Lines (0:23)
04. Maximizing the Margin (0:34)
05. Practice with Margins (0:50)
06. SVMs and Tricky Data Distributions (1:00)
07. SVM Response to Outliers (1:09)
08. SVM Outlier Practice (0:57)
09. Handoff to Katie (0:10)
10. SVM in SKlearn (1:44)
11. Coding Up the SVM (2:13)
12. Nonlinear SVMs (0:56)
13. Nonlinear Data (0:49)
14. A New Feature (1:24)
15. Visualizing the New Feature (1:09)
16. Separating with the New Feature (0:44)
New Lecture (1:48)
18. Kernel Trick (1:39)
19. Playing Around with Kernel Choices (2:53)
20. Kernel and Gamma (1:20)
21. SVM C Parameter (2:24)
22. SVM Gamma Parameter (1:59)
23. Overfitting (1:34)
Decision Trees
02. Linearly Separable Data-YNfxSsQT78Y (0:06)
02. Linearly Separable Data-lCWGV6ZuXt0 (1:16)
03. Multiple Linear Questions-p_xPoBRJdtE (0:59)
03. Multiple Linear Questions-t1Y-nzgI1L4 (0:47)
04. Constructing a Decision Tree First Split-GMe5JT2_oUE (0:27)
04. Constructing a Decision Tree First Split-iZYv1WdWwQo (0:38)
05. Constructing a Decision Tree 2nd Split-CIxvkVy1UBI (0:16)
05. Constructing a Decision Tree 2nd Split-U2yZxIeG2t0 (0:16)
06. Class Labels After Second Split--3VPMBIwTtE (0:12)
06. Class Labels After Second Split-A7KKnDmZBA0 (0:10)
07. Constructing A Decision TreeThird Split-1GCPKAYDPTg (0:50)
07. Constructing A Decision TreeThird Split-RxySNoOmXnc (0:08)
08. Coding A Decision Tree-cxV6OAxCfIQ (1:27)
08. Coding A Decision Tree-YaZu4waSryo (1:52)
09. [object Object]-i7pRvuVoWg0 (0:13)
09. [object Object]-sCZI5gWS6mg (0:28)
10. Decision Tree Parameters-Is5T4alCCGQ (0:34)
10. Decision Tree Parameters-jkJ4dbbpVCQ (2:18)
11. Min Samples Split-Mt5TWGYacJs (0:29)
11. Min Samples Split-xU84TShi7I4 (0:34)
12. Decision Tree Accuracy-1z5mVNdF1KA (0:09)
12. Decision Tree Accuracy-EOLzooGccPc (0:58)
13. Data Impurity and Entropy-Bd15qhUrKCI (1:22)
14. Minimizing Impurity in Splitting-lfZg7j5W7u8 (0:36)
14. Minimizing Impurity in Splitting-L6J6BRFgDiI (0:35)
15. Formula of Entropy-NHAatuG0T3Q (0:52)
16. Entropy Calculation Part 1-JX3NN5zwL08 (0:02)
16. Entropy Calculation Part 1-K-rQ8KnmmH8 (0:48)
17. Entropy Calculation Part 2-3tzTP3e0Cjw (0:03)
17. Entropy Calculation Part 2-GtiLFC7EgFE (0:05)
18. Entropy Calculation Part 3-M2Sp-Y2a71c (0:13)
18. Entropy Calculation Part 3-WmnGwUCW-Yc (0:07)
19. Entropy Calculation Part 4-bhwb2v9rEdI (0:08)
19. Entropy Calculation Part 4-V0FNwMKhIVM (0:11)
20. Entropy Calculation Part 5-B_fHrMIzIgE (1:33)
20. Entropy Calculation Part 5-ZSkYbBsFuOQ (0:06)
21. Information Gain-KYieR9y-ue4 (0:44)
22. Information Gain Calculation Part 1-daVA3PI2E6o (0:29)
22. Information Gain Calculation Part 1-erdekkpG-Do (0:23)
23. Information Gain Calculation Part 2-4YP0K-5c310 (0:08)
23. Information Gain Calculation Part 2-t4qaavAslSw (0:07)
24. Information Gain Calculation Part 3-s_-I8mbrfw0 (0:06)
24. Information Gain Calculation Part 3-yWPbe8onCeA (0:07)
25. Information Gain Calculation Part 4-i6aCKjMeZPk (0:05)
25. Information Gain Calculation Part 4-j0uDMc3Yrlo (0:07)
26. Information Gain Calculation Part 5-3jfQlMLyH2o (1:11)
26. Information Gain Calculation Part 5-4oOXVejgFGk (0:07)
27. Information Gain Calculation Part 6-qnfVoUChRlQ (0:46)
27. Information Gain Calculation Part 6-zqmrW9N9WGw (0:23)
28. Information Gain Calculation Part 7-EDFp4wU5BMo (0:14)
28. Information Gain Calculation Part 7-frzL4n6Y-vU (0:03)
29. Information Gain Calculation Part 8-c7UjSq7Fmr8 (0:08)
29. Information Gain Calculation Part 8-F-xSYJ3y_pA (0:04)
30. Information Gain Calculation Part 9-PDqyWzZCVBY (0:05)
30. Information Gain Calculation Part 9-V-jzhJoeZj8 (0:15)
31. Information Gain Calculation Part 10-o75xNa_jwvg (1:08)
31. Information Gain Calculation Part 10-XYHTuv2FpWQ (0:16)
32. Tuning Criterion Parameter-V80QLNK5fFQ (0:56)
Object Detection
04. Object Detection Overview-zNBWOHycI0I (0:43)
05. Manual Vehicle Detection
06. Features-u3NOabeuMjA (0:29)
08. Color Features-JT4fDW7lsG8 (0:58)
09. Template Matching
11. Color Histogram Features-8ZvNANafMU8 (1:12)
12. Histograms of Color
14. Color Spaces-Adunl74VJIY (2:05)
13. Histogram Comparison
15. Explore Color Spaces
16. Spatial Binning of Color
17. Gradient Features-cvGtDBu8ONQ (1:09)
18. HOG Features-DNweoAqjwNQ (2:33)
19. Data Exploration
20. scikit-image HOG
Kalman Filters
02. Tracking Intro
04. Variance Comparison-TGdMG81hXc8 (0:24)
03. Gaussian Intro-6IhtnM1e0IY (2:00)
05. Preferred Gaussian--9AVZ-N_gbM (0:34)
02. Tracking Intro-BkjQzEyJWrE (1:56)
04. Variance Comparison-rczAG7meAY4 (0:53)
05. Preferred Gaussian-sBsju-6nQWI (0:11)
06. Evaluate Gaussian-4-0nBfsD4jo (0:14)
07. Maximize Gaussian-fRYtUP0P4Lg (0:52)
07. Maximize Gaussian-2cD8T65E-jM (0:16)
09. Shifting the Mean-8c479K2UCZo (1:38)
06. Evaluate Gaussian-mQtjczyAxQs (0:38)
09. Shifting the Mean-HmcurWkA0fQ (0:24)
10. Predicting the Peak-PsyqM704q2Y (0:36)
11. Parameter Update-Lwn6FJgyyYI (0:30)
10. Predicting the Peak-zc_GQiISQ3E (0:40)
11. Parameter Update-d8UrbKKlGxI (1:54)
12. Parameter Update 2-2BfisMbu86o (0:28)
12. Parameter Update 2-_AAkw_fynwc (0:57)
13. Separated Gaussians-QAqsIWVVX0Y (0:28)
14. Separated Gaussians 2-edcfMK_bKXw (0:59)
14. Separated Gaussians 2-0FmTokjoRgo (0:22)
13. Separated Gaussians-fGcozDEwnwY (0:08)
15. New Mean and Variance-yo8jf0U4hlc (1:00)
15. New Mean and Variance-SwxRWZaC1FM (0:37)
16. Gaussian Motion-X7YggdDnLaw (2:43)
17. Predict Function-AMFig-sYGfM (0:13)
17. Predict Function-DV2cX9W0tT8 (0:28)
18. Kalman Filter Code-3xBycKfnCOQ (1:33)
16. Gaussian Motion-xNPEjY4dsds (0:09)
19. Kalman Prediction-tSfmiuB9s2c (1:01)
19. Kalman Prediction-doyrdLJ6rJ4 (1:24)
20. Kalman Filter Land-LXJ5jrvDuEk (2:37)
21. Kalman Filter Prediciton-HTL5-0DDqE4 (1:32)
21. Kalman Filter Prediciton-SK3cnmu8BYU (0:27)
22. Another Prediction-cUKlYjQEQGY (0:13)
23. More Kalman Filters-hUnTg5v4tDU (3:58)
22. Another Prediction-JNDsm_Gzxi0 (0:14)
24. Kalman Filter Design-KYEr4BXhD_E (3:07)
18. Kalman Filter Code-X7cixvcogl8 (3:47)
25. Kalman Matrices-ade97UKqSIc (5:44)
25. Kalman Matrices-LEuzK9X7_hM (4:05)
26. Conclusion-6kFMxhlfHuI (0:39)
C++ Checkpoint
03. Challenge 1
04. Challenge 1 Solution
05. Challenge 2
06. Challenge 2 Solution
07. Challenge 3
08. Challenge 3 Solution
09. Challenge 4
10. Challenge 4 Solution
11. Challenge 5
12. Challenge 5 Solution
Lidar and Radar Fusion with Kalman Filters in C++
03. Lesson Map-_u8Vk58VqxY (2:04)
05. Refresh Estimation Problem-Uwq7_6slV_M (3:08)
06. ND013 M3 L4 05 L Kalman Filter Equations In C++-ZG8Ya-mCGhI (1:48)
07. Kalman Filter Equations In C++ Programming-KV4ZdUnOz9I (2:50)
07. Kalman Filter Equations in C++ Part 1
08. Kalman Filter Equations In C++ Programming-smRjTGQG2SY (0:42)
09. State Prediction-_A0NRvmgo3w (1:32)
10. Process Covariance Matrix-iFcIiqRGaws (2:56)
11. Laser Measurements-drbV05qKV8w (0:53)
12. Laser Measurements Part 2
13. Programming Assignment-gTEQHV_1E2k (4:05)
14. Programming Assignment Solution-udsB-13ntY8 (2:11)
15. Radar Measurements-LOz9AaHvB8M (3:27)
17. Extended Kalman Filter-nMUd_esBMM8 (2:48)
18. Multivariate Taylor Series Expansion
19. Jacobian Matrix-FeE5cRlMZqU (2:06)
20. Jacobian Matrix-pRhuwlMhG3o (0:29)
21. EKF Algortihm Generalization-co0ZczAuwdM (0:57)
22. Sensor Fusion General Processing Flow-dcTY4vRg5vo (1:16)
23. Evaluating The Performance-1HieeV8IUv8 (1:52)
24. Evaluating The Performance-1iVBYQ_KWXk (0:32)
Extended Kalman Filter Project
02. Intro to Extended Kalman Filter Project (0:31)
03. Data File
04. File Structure
05. Main.cpp
06. Project Code
07. Tips and Tricks
08. Project Resources
12. uWebSocketIO Starter Guide
13. Environment Setup (Windows)
16. Compiling and Running the Project
Unscented Kalman Filters
02. CTRV-g72HXEcSQHU (0:27)
03. State Vector Introduction-hLVz0YOhntA (0:53)
04. CTRV-o2HVZFSH1Fs (1:45)
05. CTRV Integral Last 3-dcR9RtwJ6yk (1:06)
06. CTRV Integral Position-9E6K4Aw_MaI (1:03)
07. CTRV Zero Yaw Rate-8gAsx7OAH6c (0:52)
08. CTRV Process Noise Effect Last 3-DUm8e7K8qZ8 (0:45)
08. CTRV Process Noise Vector-Qr99RXys-G0 (1:09)
09. 13 Q CTRV Process Noise Position-DJ_K1udemNk (1:50)
10. UKF Process Chain-sU7ifLgxxas (1:03)
11. UKF What's the Problem-OFb47Lu9JfM (3:38)
12. UKF Basics Unscented Transformation-8jbckHQDl4A (1:53)
12. UKF Basic Unscented Transformation-r594P0XjKa4 (0:49)
13. 19 UKF Generate Sigma Points-t7YJJpEzTX4 (4:13)
14. Assignment Sigma Point Generation-TIc3n-cxTqc (2:04)
16. 22 L UKF Augmentation-G-kdutCM1RQ (4:00)
17. Assignment Augmentation-5p-PqtxQeM8 (0:46)
19. Sigma Point Prediction-zeMy0dth3yI (1:17)
20. Assignment Sigma Point Prediction-RQvnRpSPUak (1:07)
22. 26 L Predict Mean And Covar-6DELFN7Fz4c (1:42)
25. Measurement Prediction-qDX8nL_OT60 (3:24)
23. Assignement Predicted Mean And Covariance-0vl_wfDpVec (0:26)
28. UKF Update-pJ5XauGNclI (1:39)
26. Assignment Predict Radar Measurement-GYQeizoj09E (0:40)
29. Assignment UKF Update-f36o4sCEQvY (1:07)
31. Parameters And Consistency-S4fX3X_9oik (6:19)
32. What to Expect from the Project-WAt_g6HgYvs (3:48)
Introduction to Localization
01. L10 Localization Overview A01 Intro To AI-8p4C7jvfwvQ (3:14)
03. Localization Intuition-mVCSCU67D80 (0:34)
04. Localization Intuition Explanation-alIgWnGBUS8 (0:48)
05. Localizing a Self Driving Car-U-uDtVgezcE (2:55)
Localization Overview
01. Introduction-Uqt_pRbR8rI (3:32)
02. Localization-31xZhj2uPr4 (2:10)
03. Total Probability-n1EacrqyCs8 (4:34)
04. Uniform Probability Quiz-IZC33Tmy8Lo (0:09)
04. Uniform Probability Quiz-6tV5NY1HoNA (0:36)
05. Uniform Distribution-_sAkAALHyEg (0:06)
06. Generalized Uniform Distribution-e21oU80gwWc (0:22)
06. Generalized Uniform Distribution-nsSvTTA0p8E (0:25)
05. Uniform Distribution-ysebYA6tDZ4 (0:19)
07. Probability After Sense-dEiQObhi2J4 (0:14)
07. Probability After Sense-UFcTLCttNRI (1:38)
08. Compute Sum-WgX17_mmc1c (0:03)
08. Compute Sum-qa9B4r5m8wM (0:23)
09. Normalize Distribution-Uc_rHR6U70U (0:21)
09. Normalize Distribution-SW_wvez0izo (0:50)
10. pHit and pMiss-FnhHQht4vDo (0:16)
10. pHit and pMiss-wOfAyDvun5w (0:19)
11. Sum of Probabilities-6c0XvswnGm0 (0:08)
11. Sum of Probabilities-z0oijOqN8K8 (0:09)
12. Sense Function-Y5iFxWRTw1c (0:46)
12. Sense Function-eIjyrQpDogg (1:20)
13. Normalized Sense Function-GqWszyHTYas (0:13)
13. Normalized Sense Function-UX3W8TUKbJ0 (0:27)
14. Test Sense Function-F8AHaaJVmkw (0:22)
14. Test Sense Function-Lf2DYUCsUH4 (0:51)
15. Multiple Measurements-gDO4sF8gR9k (0:26)
15. Multiple Measurements--3qTapGGa-8 (0:42)
17. Exact Motion-Iky7rJXQU_4 (0:25)
17. Exact Motion-1mL6CtD3rAM (0:37)
18. Move Function-TnFq6hufsYs (1:04)
18. Move Function-wfjE0mVADIk (0:57)
19. Inexact Motion 1-C3f-T9_GTpw (1:40)
19. Inexact Motion 1-mGWGhgZG_jM (0:12)
20. Inexact Motion 2-gZbPZLFKS68 (0:17)
20. Inexact Motion 2-jR7FERpsqe4 (1:03)
21. Inexact Motion 3-BldUOLB2U1Y (0:09)
21. Inexact Motion 3-7T1Rr7KLgdM (1:21)
22. Inexact Move Function-68Kao9dkIKA (0:14)
22. Inexact Move Function-QCnPJcNprEU (0:46)
23. Limit Distribution Quiz-SXSafquSoW8 (1:55)
23. Limit Distribution Quiz-kfPWiMsnWFI (1:00)
24. Move Twice-oqlgQa1IdcY (0:12)
24. Move Twice-sKiumVTdpgY (0:14)
25. Move 1000-nYt9b_pNvEE (0:12)
25. Move 1000-x2o1g3J-1nw (0:06)
26. Sense and Move-K8g3Hss8Q1A (1:48)
26. Sense and Move-1s2dRczcu1A (1:08)
27. Sense and Move 2-rmWL_3r8MKo (1:56)
27. Sense and Move 2--wT7h9Gdm_8 (0:12)
28. Localization Summary-MVbo4OAgQCc (1:06)
29. Formal Definition of Probability 1--F2gJXWbN6s (0:26)
29. Formal Definition of Probability 1-OQ2JS2wQzrs (0:05)
30. Formal Definition of Probability 2-uw51WQDqXAI (0:03)
30. Formal Definition of Probability 2-PE-k3PGXeLY (0:06)
31. Formal Definition of Probability 3-TF6AWXSlOcY (0:12)
31. Formal Definition of Probability 3-oDPbdGXH5nE (0:10)
32. Bayes' Rule-sA5wv56qYc0 (3:03)
33. Cancer Test-OgC5M2XdIac (1:22)
33. Cancer Test-SZ6Jg1wS604 (1:13)
34. Theorem of Total Probability-byZ-BzbQA5M (2:00)
35. Coin Flip Quiz-ASUXN9Ay35M (0:58)
35. Coin Flip Quiz-hzDsYZ61D5M (0:32)
36. Two Coin Quiz-_AhoOd8YUK0 (1:11)
36. Two Coin Quiz-2PZHPjyYnMg (0:39)
Markov Localization
02. Markov Location Lesson Overview-rSj5lpzliQg (0:37)
03. ND013 M4 L3 02 L Localization Posterior-WCva9DtGgGA (2:06)
04. 03 L Explain Localization Posterior V2-lGpIgbA5ZdA (3:08)
05. Bayes' Rule
06. Bayes' Filter For Localization
07. Calculate Localization Posterior
08. Initialize Belief State
09. Initialize Priors Function
11. Quiz How Much Data-wzcFHAf-9lo (0:36)
12. 03.5 S How Much Data-PQV6gWuyVOs (0:44)
13. Derivation Outline-coHodx-I56U (0:57)
14. Apply Bayes Rule With Additional Conditions-RsHS2o3zjcw (2:30)
15. Explain Bayes Rule And Apply Law Of Total Probability-p2qfHa9G7_k (1:58)
16. Explain Law Of Total Probability And Markov Assumption-9hGU7s5m8c0 (1:59)
17. Markov Assumption for Motion Model: Quiz
18. Explain Markov Assumption For Motion Model-YFLAFptKU5E (5:02)
19. After Applying Markov Assumption: Quiz
20. Explain Recursive Structure-d0GrWJeVFjU (2:38)
21. Lesson Breakpoint
22. Implementation Details For Motion Model-O47bOcJm1eE (1:11)
23. ND013 M4 L3 15.5 Q Noise In Motion Model-zRbT36RTlhs (0:42)
24. Noise In Motion Model Solution-zJ9NWz7IlOM (0:34)
26. Motion Model Probabiity I
27. Motion Model Probability II
28. Coding the Motion Model
30. Observation Model Intro-SDM1aVqRBCk (0:59)
31. Markov Assumption For Observation Model-dyDjINdrIz0 (3:27)
32. Finalize The Bayes Localization Filter-teVw2J-_6ZE (1:35)
33. Bayes Filter Theory Summary-lMyu2-PZGuk (1:11)
34. Observation Model Probability
35. Get Pseudo Ranges
37. Coding the Observation Model
38. Coding the Full Filter
Motion Models
02. Lesson Introduction--bPE6USDH3A (0:52)
03. Motion Models-B2bXg8LaeF0 (1:15)
04. Yaw Rate Velocity-tS7BYlCo3nU (1:19)
07. Odometry-IusJ3cTusp8 (0:49)
09. Odometry Errors Solution-FS_5mHoszx8 (1:18)
Particle Filters
01. Field Trip-2ocy_7PJtfA (2:02)
02. State Space-oyw7WEHMvVY (0:11)
02. State Space-G7nvigL0aDw (1:01)
03. Belief Modality-NhKyyhNl70A (0:22)
03. Belief Modality-5vdbYPc7tWw (0:23)
04. Efficiency-7CNEY8lRrGE (0:34)
04. Efficiency-rA8ZpMR6yXM (0:54)
05. Exact or Approximate-1N3_RnTDFqU (0:35)
05. Exact or Approximate-WKlm2aO2QGY (0:18)
06. Particle Filters-4S-sx5_cmLU (3:46)
07. Using Robot Class-1hgVZtRIjFU (2:00)
08. Robot Class Details-ZFqEh8JylvI (0:42)
09. Moving Robot-SFcHsK2SWrI (1:07)
09. Moving Robot-_37pf6lV15s (0:29)
10. Add Noise-FQEeI3qzaOM (0:19)
10. Add Noise-ajOKsQLxoJI (0:36)
11. Robot World-qq5h-Xw4DGg (0:37)
12. Creating Particles-JNI9O9FjfDQ (0:49)
12. Creating Particles-dH6uzx78lBA (1:30)
13. Robot Particles--gNoDMlRwyc (0:53)
13. Robot Particles--HQf6pkcebQ (0:42)
14. Importance Weight-xP9PrSTJPz0 (5:36)
14. Importance Weight-VJvBzdTPlAQ (0:58)
15. Resampling-FjRX_i3SsJA (0:35)
15. Resampling-zlCJQmxvrkE (2:31)
16. Never Sampled 1-8ffPkDiDioI (0:13)
16. Never Sampled 1-MhhM1uh0-3w (0:11)
17. Never Sampled 2-i457B5Iyg-8 (0:12)
17. Never Sampled 2-q95KMAIqDDY (0:15)
18. Never Sampled 3-Z1oQl-1cUeE (0:18)
18. Never Sampled 3-hcoKwWBvB6Y (1:22)
19. New Particle-LJXbHoq5EZk (0:41)
19. New Particle-AROtzVxDDx4 (0:58)
20. Resampling Wheel-wNQVo6uOgYA (3:12)
20. Resampling Wheel-aHLslaWO-AQ (3:22)
21. Orientation 1--lq0uzHd9T0 (0:42)
21. Orientation 1-cupiUHaKvdI (0:18)
22. Orientation 2-17FoJwLiQkg (0:04)
22. Orientation 2-Ex0su1DnIuw (1:00)
23. Error-3kOrzhYCXz8 (1:26)
23. Error-UAdcKWLi9G8 (1:47)
24. You and Sebastian-gTMe0E6SM_M (0:48)
25. Filters-bjZy-RVms_8 (2:53)
26. 2012-QgOUu2sUDzg (1:58)
25. Filters-d_DXbkU7iPY (0:32)
Implementation of a Particle Filter
02. Lesson Introduction-_VjhAIChVcI (0:48)
03. Pseudocode-JNm1fnWj5To (0:55)
04. Initialization-agPdu0c5_GM (2:05)
05. Program Gaussian Sampling: Code
07. Prediction Step-kNthLZTHDIM (0:42)
10. Data Association- Nearest Neighbor-nG_pFGT-fuo (1:59)
11. Nearest Neighbor Advantages And Disadvantages-snXId_LyzXs (3:34)
12. Update Step-1Uq2QZKz3aI (4:05)
13. Calculating Error-HiRrJYZr-0I (1:30)
14. Transformations and Associations
15. ND013 M4 L6 Converting Landmark Observations-BrQfVd4JXpg (3:53)
20. Particle Weights Solution
21. Explanation of Project Code-3VRp4chnPE4 (5:19)
Kidnapped Vehicle Project
02. Project Introduction
03. Particle Filter Project Visualizer (0:49)
PID Control
02. PID Control - Artificial Intelligence for Robotics--8w0prceask (1:14)
03. Proportional Control - Artificial Intelligence for Robotics-gGo-gSFqYqg (0:34)
04. Implement P Controller - Artificial Intelligence for Robotics-OrJgrTc5d04 (2:08)
05. Implement P Controller Solution - Artificial Intelligence for Robotics-wvdFPAOCb64 (0:25)
06. Oscillations - Artificial Intelligence for Robotics-CO3zjkxBaIc (0:18)
07. PD Controller - Artificial Intelligence for Robotics-kVYy2kjZjhA (2:34)
09. Systematic Bias - Artificial Intelligence for Robotics-1wxFEcqq3_c (0:50)
08. PD Controller Solution - Artificial Intelligence for Robotics-YgomQgfFlTQ (0:27)
10. Is PD Enough - Artificial Intelligence for Robotics-gDbpwPdStlY (0:11)
11. PID Implementation - Artificial Intelligence for Robotics-Ag8H3Iit9j4 (1:43)
12. PID Implementation Solution - Artificial Intelligence for Robotics-dgZnqCfyCoA (1:30)
13. Twiddle - Artificial Intelligence for Robotics-2uQ2BSzDvXs (3:19)
14. Parameter Optimization - Artificial Intelligence for Robotics-A2b3F5Ae53Y (2:10)
15. Parameter Optimization Solution - Artificial Intelligence for Robotics-YQ5Pa-OKQm0 (2:33)
Vehicle Models
02. Vehicle Models
03. State-6vFczwAYjsU (0:27)
04. 04 L Building A Kinematic Model--Nnk8n81zr4 (0:44)
05. Global Kinematic Model
06. Solution: Global Kinematic Model
07. Following Trajectories-sOSHaAf_7b8 (0:30)
08. Fitting Polynomials
09. Solution: Fitting Polynomials
10. Errors-qtg_HiqoGHY (0:32)
11. Dynamic Models
12. Dynamic Models - Part 1 Forces-KRN7GVJkFnU (0:32)
13. Dynamic Models - Part 2 Slip Angle-oDusBbn820k (0:32)
14. Dynamic Models - Part 3 Slip Ratio-kSqOJDwRFVc (0:21)
15. Dynamic Models - Part 4 Tire Models-OFIL0yqsV7o (0:33)
16. 15 L Actuator Constraints-EwcDwdM1msg (0:51)
Model Predictive Control
02. Reference State
03. Dealing With Stopping-2gkRWj7KIMU (0:27)
04. Additional Cost Constraints-lsdZtPPOhtk (0:35)
05. Length and Duration
06. Putting It All Together-CZ71uEy8EtI (0:53)
07. Latency
08. Mind The Line
09. Solution: Mind The Line
10. Tuning MPC
11. Tips and Tricks
Search
04. Motion Planning-KHAu5A_flcQ (2:06)
05. Compute Cost-7-yOaHVeATk (0:25)
05. Compute Cost-dBA94pR6JYw (0:48)
06. Compute Cost 2-OXIESpN0KaE (0:22)
06. Compute Cost 2-n9_th4V4qE4 (0:38)
07. Optimal Path-Exl_kCyUc8U (0:47)
07. Optimal Path-wLz-nF2CrHc (0:52)
08. Optimal Path 2-qFswCrEUZSM (0:22)
08. Optimal Path 2-GvCSZOR3hLQ (0:07)
09. Maze-ge_-o0RfrgM (0:32)
09. Maze-yVh0lVlerWs (0:16)
10. Maze 2-YwAyqkznxa0 (1:11)
10. Maze 2-aBUxPyEDOWw (0:10)
11. First Search Program - Artificial Intelligence for Robotics-TPIFP4E7DVo (6:55)
11. First Search Program Solution - Artificial Intelligence for Robotics-cl8Kdkr4Gbg (5:04)
12. Expansion Grid Solution - Artificial Intelligence for Robotics-pH6sDfBalaw (0:43)
12. Expansion Grid - Artificial Intelligence for Robotics-1l7bWfz8sJw (1:24)
13. Print Path-6UJFZf40aBg (1:41)
13. Print Path-CyQ2gl-9W4o (3:00)
14. A-lxCCtgHk5Vs (8:27)
15. Implement A-SSyvcCZKlqo (3:54)
15. Implement A-V0Ppaw5G2Pg (0:31)
16. A in Action-qXZt-B7iUyw (3:33)
17. Dynamic Programming-r2bPY2s9wII (3:14)
18. Computing Value-ebFQqd7Uhek (0:13)
18. Computing Value-Sn-ZUbZdOn8 (2:28)
19. Computing Value 2-t2aT92C2ruA (0:12)
19. Computing Value 2-yTV3JPJk1kE (0:03)
20. Value Program-RXpuBRA-cpo (1:21)
20. Value Program-FdT1g_Bzjm0 (2:41)
21. Optimum Policy-MMDcirk9QPM (1:00)
21. Optimum Policy-7kllZxX-Nso (1:05)
22. Left Turn Policy-rH5DKpwYQLY (3:25)
22. Left Turn Policy-bQA2ELDNmmg (3:57)
23. Planning Conclusion-M7ZJ74RVHqo (2:08)
Prediction
01. 01 L Introduction And Overview-aHmVFZ6hMjc (4:52)
02. I/O Recap
03. 04 L Model Vs Data Driven Approaches-ehfA_NC7Ka4 (2:17)
05. 06 L Data Driven Example Trajectory Clustering-jbFeQ9P2V9A (3:06)
06. 07 L TrajectoryClustering2 - Online Prediction-UPiED4soM4w (2:34)
07. 08 L ThinkingAboutModelBasedApproaches-2JHmXN4AKNY (3:19)
08. Frenet Coordinates
09. 09 L ProcessModels-VcRDsKBn7tc (4:36)
10. More on Process Models
11. 11 L MultimodalEstimationApproaches-u1Tmt0Qdlgk (2:55)
12. Summary of Data Driven and Model Based Approaches
13. 13 L Overview Of Hybrid Approaches-yCRvxI5wJS0 (1:19)
14. 14 L IntroToNaiveBayes-AkrC_WP1MWk (2:56)
16. Implement Naive Bayes C++
Behavior Planning
04. 03 L The Behavior Problem-5t-oVAZagT8 (2:22)
02. 01 L Lesson Outline-qyH-1BMCiUY (1:43)
08. 07 L StatesForASelfDrivingCar-zoN0-IPe0I4 (2:33)
06. 05 L Formalizing FSMs-sEZn3iZgOaI (1:40)
10. 09 Q InputsToTransitionFunctions-8jStt2d_SYc (0:32)
09. 08 L StatesForASelfDrivingCarSolution-QXU6ptbxfyo (5:03)
12. 12 L CreateACostFunctionSpeedPenalty-wGRDT2wTnn8 (2:32)
20. 16 L SchedulingComputeTime-N6AlIUczqRM (1:50)
10. 09 S InputsToTransitionFunctions-AjMSl8zR-P0 (0:07)
18. 14 L CostFunctionDesignWeightTweaking-NK6SP-r4dGs (5:57)
Trajectory Generation
03. 02 L The Motion Planning Problem-daGIOru4Bi4 (2:10)
04. 03 L Properties Of Motion Planning Algorithsm-lNpD43L0qvw (1:00)
05. Types of Motion Planning Algorithms-6-K1aLTEvk8 (3:32)
06. A - Artificial Intelligence for Robotics-lxCCtgHk5Vs (8:27)
07. 06 L A- Reminder Solution-HtQjw7qr2-o (1:45)
08. Hybrid A Introduction-NuurQejBk0o (2:58)
10. 09 L Hybrid A- Tradeoffs Solution-6yAdF5u2B04 (0:58)
11. 10 L Hybrid A- In Practice-Mkz_WjyRzag (5:32)
16. 15 L EnvironmentClassification-4NOvHff7WFQ (1:09)
17. 16 L Frenet Reminder-u3TYp-hDojk (1:23)
18. 17 L The Need For T-4WsqXkB8zqQ (3:22)
19. 18 L S D And T-SD1iyzgFf8s (3:50)
21. 20 L Structures Trajectory Generation Overview-N2cwqKR63x8 (0:49)
22. 21 L TrajectoriesWithBoundaryConditions-p73Jma9nW-Q (3:21)
23. 22 Jerk Minimizing Trajectories-pomDFkzy2bk (4:08)
24. 23 L DerivationOverview-TuVp_HhQq7A (2:00)
26. 25 L How Polynomial Trajectory Generation Works-5ZzYOqYZZ3I (1:11)
29. 27 L WhatShouldBeCheckedSolution-E2TBXjyYb_Y (0:48)
30. 29 L Implementing Feasibility-8tD8Os9_gKc (2:09)
31. 31 L PuttingItAllTogether-UhrmXmnKhQE (4:23)
Path Planning Project
02. Getting Started
03. More Complex Paths
04. Highway Map
Fully Convolutional Networks
02. Why Fully Convolutional Networks (FCNs) -WQ_YOz1o9GM (1:36)
03. Fully Convolutional Networks-_Lh2ozg5yTs (1:01)
04. Fully Connected to 1x1 Convolution-xbPtOhkJW1A (0:53)
07. Transposed Convolutions-K6mlLX8ZZDs (0:35)
10. Skip Connections-JUYLA5PWzo0 (1:01)
11. FCNs In The Wild-q9wTd53-hsw (1:06)
Scene Understanding
02. Bounding Boxes-uPv4d0Xl8hc (0:52)
03. Semantic Segmentation-_L5gJnZrw48 (0:35)
04. Scene Understanding-aMQREc-mP50 (0:34)
05. IoU---9BTjOsO6U (1:29)
Inference Performance
02. Why Bother With Performance-pCg0q8qmgsk (0:38)
03. L3 03 L Semantic Segmentation Revisited-xFcI26kLtiY (0:35)
04. Interlude Using The AMI (4:00)
04. P1-Y2ggt6l7PTo (0:57)
04. P2-Yr_rRyEOTnM (1:05)
05. Freezing Graphs
06. Graph Transforms
07. Fusion-JOksFH3vQgk (1:39)
08. Optimizing For Inference
09. Reducing Precision-bZPG5I_igR8 (1:41)
12. 8-bit Calculations
13. Compilation
14. AOT & JIT
15. Reusing The Graph
Introduction to Functional Safety راجعوا
03. What Is Safety-o1mdbufGaq8 (1:18)
04. What Is Functional Safety-NeUXHSv5qz8 (1:19)
05. Introduction To Identify Hazards-ZzgEw7WQgTs (1:17)
07. Introduction To Evaluating Risks-3N-6YK_pPzA (2:26)
08. L1 17 Reducing Risk With System Engineering-tFxkJKcujhQ (3:26)
09. L1 19 Introduction To Iso26262-bjvpmBOG60Q (2:01)
Functional Safety Safety Planراجعا
02. L2 03 L Safety Culture-hca5xarxPVM (1:09)
03. L2 05 L Tailoring The Safety Lifecycle-Ym9raP5zb2U (1:48)
04. L2 06 L Safety Management Roles And Responsibilities-5fViXy7XF0w (1:09)
05. L2 08 L Development Interface Agreement-xF79RP2LduY (1:38)
06. L2 10 L Confirmation Measures-3XiALMje_5Q (0:43)
Functional Safety Hazard Analysis and Risk Assessment
04. L3 04 L Introduction To HARA-Qxui0XShsbE (1:28)
06. 05 HARA Situational Analysis-o9iBiz7IQ_k (2:29)
07. L3 09 L HARA Identification Of Hazards-LXp7ScZaKp4 (1:48)
08. L3 14 HARA Risk Assessment, Severity, Exposure, Controllability-44NYK53gOAM (1:50)
08. L3 14 Hara Risk Part 2--3EXJjR6fbk (0:53)
08. L3 14 L Hara Risk Part 3-buFLRLfwSWw (0:59)
09. L3 18 L HARA ASIL Levels-l7vx-w06fZw (1:54)
10. L3 20 L HARA Safety Goals-lMT1EB5cZR8 (0:50)
Functional Safety Functional Safety Concept
01. L4 01 L Introduction-srUVh4mBvws (0:35)
03. L4 03 Functional Safety Requirements-9z5YqMYH7mY (1:17)
04. L4 06 Allocation Of Requirements To Architecture-cDkQLZ3PJqM (2:18)
05. Architecture Refinement-SK85D4cvwXo (1:11)
06. L4 10 Function Safety Requirements And ASIL Inheritance-oeKSXaP7Lxg (1:36)
07. L4 13 ASIL Decomposition-dphScT1QTNY (2:26)
08. L4 15 Fault Tolerant Time Interval-o4PzRfVN_to (2:00)
09. L4 18 Warning And Degradation Concept-khNhy3IwKa0 (1:24)
Functional Safety Technical Safety Concept
02. L5 03 Deriving Techincal Safety Requirements From Functional Safety Requirements-fVLsG83a-So (1:30)
03. L5 07 Other Types Of Techincal Safety Requirements-9xIlkXobXS0 (3:24)
04. L5 09 Technical Safety Requirement Attributes-EOYTl2e8wEs (1:54)
05. L5 12 Allocation Of Requirements To System Acrchitecture Elements-dA2up9vZCcM (2:29)
Functional Safety at the Software and Hardware Levels
03. L6 04 Hardware Failure Metrics-wlCVuAJj1xk (0:55)
04. L6 06 Programming Languages-KpAhkXNan7Y (1:12)
05. What Is MATLAB SelfDriving-6J3Ho1ZrHm0 (1:39)
06. L6 09 Software Safety Requirments Architecture Testing And Intergration-OJMGRtJciNI (1:39)
08. L6 11-12 Software Safety Robustness And Quality-RQEnvtti3sM (2:05)
09. L6 13 Freedom From Interference Spatial-HraIGQSxsQ0 (1:29)
10. L6 15 Freedom From Interference Temporal-ChGZCPXko7M (1:43)
11. L6 17 Freedom From Interference Temporal Part 2-wRbGk0SwWNQ (1:28)
13. L6 18 Freedom From Interference Communication-J2T842SLPgs (1:21)
14. L6 20 System Architecture Safety Design Patterns-9q8WAW-g8jE (1:23)
Introduction to ROS راجع
04. Build Robots with ROS-7eaz0qW7y_I (2:05)
05. Brief History of ROS-Cw-FEyqU2NI (0:51)
07. Message Passing-IpNp13F-TgQ (1:18)
06. Nodes And Topics-t5xWx5Zgmk0 (2:35)
08. ROS Services-EXYmvpcOnCc (1:27)
09. Compute Graph-dWc4ktFohNg (1:10)
16. Turtlesim Comms List Active Nodes-J_5JTUi7sQQ (1:00)
15. Run Turtlesim-hCkE973oY9o (2:21)
18. Turtlesim Comms Get Info-Y6rMQreuOL4 (0:55)
14. Sourcing The ROS Environment-6cHlu-KVi98 (1:24)
17. Turtlesim Comms Topics-46YAnfvhTMc (0:53)
20. Turtlesim Comms Echo Messages-HNA7eKhYcyA (2:24)
19. Turtlesim Comms Message Information-f89-UgEb8Y0 (2:46)
Packages Catkin Workspaces
02. Overview of Catkin Workspaces and Packages-VqYNipeW72o (0:58)
04. Adding a Package-UJlCdokCJJ0 (1:10)
05. Roslaunch-EsGNppn8UlQ (2:35)
06. Rosdep-Kei6k78fydE (2:09)
Writing ROS Nodes
05. Simple Arm-Ki5LkE_xir4 (0:17)
05. L3 Simple Mover Code-jEO_4xxA_mI (3:38)
08. L3 Arm Mover The Code-0Li845bwxyM (4:06)
12. Look Away The Code -pOZW8SdyYsk (3:14)
System Integration Project
03. Traffic Lights 2-PzIRniXv0z0 (2:13)
06. SDC Capstone Portfolio Part 1 V1 V2-6GIFyUzhaQo (14:30)
08. SDC Capstone Portfolio Part 2 V2-kdfXo6atphY (12:20)
10. SDC Capstone Portfolio Part 3 V1 V1-oTfArPhstQU (10:39)
12. SDC Capstone Portfolio Part 4 V1-2tDrj8KjIL4 (9:59)
List-Based Collections
02. Lists-KUQSgUMtyv0 (1:09)
03. Arrays-OnPP5xDmFv0 (2:41)
05. Linked Lists-zxkpZrozDUk (1:23)
06. Linked Lists in Depth-ZONGA5wmREI (2:39)
08. Stacks-DQoCO8aGcNc (0:46)
09. Stacks Details-HpaVHzDeZC4 (1:28)
11. Queues-XAbzlilAHZw (2:06)
Searching and Sorting
01. Binary Search-0VN5iwEyq4c (1:28)
02. Efficiency of Binary Search-7WbRB7dSyvc (8:44)
04. Recursion-_aI2Jch6Epk (6:51)
06. Intro to Sorting-Z6yuIen71zM (1:57)
07. Bubble Sort-h_osLG3GmjE (2:32)
08. Efficiency of Bubble Sort-KddkHygi7is (2:25)
10. Merge Sort-K916wfSzKxE (4:16)
11. Efficiency of Merge Sort-HKiK5Y-YSkk (4:58)
13. Quick Sort-kUon6854joI (3:22)
14. Efficiency of Quick Sort-aMb5GHPGQ1U (2:52)
Maps and Hashing
01. Introduction to Maps-JEw3iQAnGKQ (0:48)
02. Sets and Maps-gmIb-qZhTDQ (1:31)
04. Introduction to Hashing-8yik3RlDFgM (1:20)
05. Hashing-kCPFfHx_LgQ (2:21)
06. Collisions-BUaWIjZ_ToY (2:49)
08. Hash Maps-A-ahUVi8pYQ (1:02)
09. String Keys-WyFwieF1NN4 (2:15)
Trees
01. Trees-PXie7f22v2Q (0:39)
02. Tree Basics-oaxLPzaXRDc (1:27)
03. Tree Terminology-mPUsDUR_sj8 (1:42)
05. Tree Traversal-KZOdmzypynw (1:48)
06. Depth-First Traversals-wp5ohHFTieM (2:35)
08. Search and Delete-KbL-HK3ztX8 (1:26)
09. Insert-j6PkPa2ZHWg (2:10)
10. Binary Search Trees-7-ZQrugO-Yc (0:39)
12. BSTs-abRNGLhGUmE (1:51)
13. BST Complications-pcB0wV7myy4 (0:54)
15. Heaps-M3B0UJWS_ag (2:14)
16. Heapify-CAbDbiCfERY (1:18)
17. Heap Implementation-2LAdml6_pDY (1:37)
18. Self-Balancing Trees-EHI548K3jiw (1:44)
19. Red-Black Trees - Insertion-dIuWLtWnkgs (1:49)
20. Tree Rotations-O5Yl-m0YbVA (1:49)
Graphs
01. Graph Introduction-DFR8F2Q9lgo (0:46)
02. What Is a Graph-p-_DFOyEMV8 (2:39)
03. Directions and Cycles-lF0vUktQDPo (1:58)
04. Connectivity-4x6u2KtNDg4 (1:19)
06. Graph Representations-uw9u6dtl0WA (1:56)
07. Adjacency Matrices-FsFhoTALA1c (1:47)
09. Graph Traversal-Dkt-XxHZaZE (0:45)
10. DFS-BC8jEidd2EQ (2:23)
11. BFS-pol4kGNlvJA (1:36)
13. Eulerian Path-zS34kHSo7fs (2:31)
Case Studies in Algorithms
04. Knapsack Problem--xRKazHGtjU (2:04)
02. Shortest Path Problem-huKUM97Vve8 (0:59)
06. Dynamic Programming-VQeFcG9pjJU (2:34)
05. A Faster Algorithm-J7S3CHFBZJA (2:46)
08. Exact and Approximate Algorithms-3A8YqOYlAwQ (2:58)
03. Dijkstra's Algorithm-SoPMK03cOgk (2:30)
14. Cropping Images in Keras
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