(Coursera) Neural Networks and Deep Learning

t 01-Module 01-Lesson 01_Welcome

A Message from the Professor
Welcome-jHA__A61nqc

01:43
Meet Your Instructors-QiflJFVOt18

00:44
Overview-RZ5iolr4RGs

01:35
What Projects Will You Build-JGpXenoW0dk

03:26
Meet the Careers Team-cuKecPpZ7PM

02:11
The Great Robot Race-saVZ_X9GfIM

53:16

t 01-Module 01-Lesson 03_Computer Vision Fundamentals

01 Introduction A01 Power Of Cameras-lCPWJEEzUeo

00:23
lane line intro-aIkAcXVxf2w

00:55
color selection-bNOWJ9wdmhk

00:44
region select-ngN9Cr-QfiI

00:18
Computer Vision-wxQhfSdxjKU

00:38
canny and gradient-LQM–KPJjD0

00:57
Canny Edge Detection-Av2GsgQWX8I

01:22
Hough Intro-JFwj5UtKmPY

01:00
Hough Quiz 5-upKjISd3aBk

00:09
sine Hough-XQf7FOhwOVk

01:13

t 01-Module 01-Lesson 04_Finding Lane Lines Project

t 01-Module 02-Lesson 01_Career Services Available to You

t 01-Module 03-Lesson 01_Introduction to Neural Networks

03 Deep Learning A01 Neural Network Intuition-UKEIHK5IifI

00:57
Module Introduction-uyLRFMI4HkA

01:29
ND013 01 L Intro To Neural Networks-UIycORUrPww

01:11
DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q

01:04
Solution Housing Prices-uhdTulw9-Nc

00:37
Classsification Example-Dh625piH7Z0

01:38
Classification Example-46PywnGa_cQ

01:03
Linear Boundaries-X-uMlsBi07k

02:43
09 Higher Dimensions-eBHunImDmWw

02:01
DL 06 Perceptron Definition Fix V2-hImSxZyRiOw

03:54
Why Neural Networks-zAkzOZntK6Y

00:46
AND And OR Perceptrons-45K5N0P9wJk

02:06
XOR Perceptron-TF83GfjYLdw

00:35
07 Perceptron Algorithm Trick-lif_qPmXvWA

02:59
DL 10 S Perceptron Algorithm-fATmrG2hQzI

00:12
Perceptron Algorithm–zhTROHtscQ

01:37
Perceptron Agorithm Pseudocode-p8Q3yu9YqYk

02:14
Non-Linear Regions-B8UrWnHh1Wc

01:00
Error Functions-YfUUunxWIJw

00:33
Error Functions-jfKShxGAbok

05:50
Discrete vs Continuous-rdP-RPDFkl0

00:18
Discrete vs. Continuous-Rm2KxFaPiJg

03:58
DL 18 Q Softmax V2-RC_A9Tu99y4

03:28
DL 18 S Softmax-n8S-v_LCTms

01:39
Quiz – Softmax-NNoezNnAMTY

00:16
One-Hot Encoding-AePvjhyvsBo

01:19
Maximum Likelihood 1-1yJx-QtlvNI

01:02
Maximum Likelihood 2-6nUUeQ9AeUA

03:08
Quiz – Cross 1–xxrisIvD0E

00:34
Quiz Cross Entropy-njq6bYrPqSU

01:33
Cross Entropy 1-iREoPUrpXvE

03:17
CrossEntropy V1-1BnhC6e0TFw

05:32
Formula For Cross 1-qvr_ego_d6w

00:21
DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees

03:11
DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc

01:08
Error Function-V5kkHldUlVU

03:45
Gradient Descent-rhVIF-nigrY

02:51
Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA

02:37
Continuous Perceptrons-07-JJ-aGEfM

00:54
Non-Linear Data-F7ZiE8PQiSc

00:20
Non-Linear Models-HWuBKCZsCo8

00:52
29 Neural Network Architecture 2-FWN3Sw5fFoM

02:09
Combinando modelos-Boy3zHVrWB4

03:47
Layers-pg99FkXYK0M

02:08
Multiclass Classification-uNTtvxwfox0

01:19
DL 41 Feedforward FIX V2-hVCuvMGOfyY

04:19
DL 42 Neural Network Error Function (1)-SC1wEW7TtKs

01:20
Multilayer perceptrons-Rs9petvTBLk

01:00
Backpropagation V2-1SmY3TZTyUk

05:12
Calculating The Gradient 1 -tVuZDbUrzzI

02:20
Chain Rule-YAhIBOnbt54

01:14
DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4

04:37
Conclusion-m8xslYUBXYo

00:15

t 01-Module 03-Lesson 02_MiniFlow

t 01-Module 03-Lesson 03_Introduction to TensorFlow

03 Deep Learning A05 Deep Learning Foundations-Fw6cM2mpfcs

00:27
Introduction To Deep Learning-7xRwuECaXBs

00:57
What Is Deep Learning-INt1nULYPak

00:53
Solving Problems – Big And Small-WHcRQMGSbqg

01:28
Let’S Get Started-ySIDqaXLhHw

00:48
Supervised Classification-XTGsutypAPE

00:47
Let’s make a deal-l1xMgirpwCU

00:36
Training Your Logistic Classifier-WQsdr1EJgz8

01:46
One-Hot Encoding-phYsxqlilUk

00:26
Cross Entropy-tRsSi_sqXjI

01:39
16 L Minimizing Cross-Entropy-YrDMXFhvh9E

01:54
17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA

00:27
Numerical Stability-_SbGcOS-jcQ

00:29
Normalized Inputs And Initial Weights-WaHQ9-UXIIg

02:46
21 L Measuring Performance-byP0DJImOSk

03:33
Transition Overfitting – Dataset Size-Lmxem7ud9yk

00:33
21 Validation And Test Set V2-iC2QOiavbrw

00:41
Validation Set Size–2XvoG6WD9k

00:24
Validation Set Size-P4LwoQyJSMk

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Validation Test Set Size Continued-cgoB-MJObmw

00:42
29 L Optimizing A Logistic Classifier-U_7nO1dm2tY

00:27
30 L Stochastic Gradient Descent-U9iEGUd9kJ0

02:25
31 L Momentum And Learning Rate Decay-O3QYdmQjXds

01:30
32 L Parameter Hyperspace!-5a3-iIhdguc

01:29

t 01-Module 03-Lesson 04_Deep Neural Networks

03 Deep Learning A07 Let’S Go Deeper-SzTpc_EWbDs

00:15
Intro To Deep Neural Networks-SXtXg_BB4lI

00:34
Number of Parameters-8cIlVoH5dhw

00:19
Number of Parameters-TkaTTptnYdA

00:23
Linear Models Are Limited-12AYOYDrpfQ

01:26
Rectified Linear Units-2-GirRl7TqU

00:25
Rectified Linear Units-z9crz_gwGCM

00:17
Network Of ReLUs-mJk1UvhDb1g

00:45
No Neurons-svA0HOjFFl0

00:27
ChainRule-DxOg_olir0k

00:23
10 Backprop-wSXcrBbY8oE

01:54
Training a Deep Learning Network-CsB7yUtMJyk

01:15
Regularization Intro-pECnr-5F3_Q

00:56
Regularization-QcJBhbuCl5g

00:59
Regularization-Quiz-E0eEW6V0_sA

00:24
Dropout-6DcImJS8uV8

01:43
Dropout Pt. 2-8nG8zzJMbZw

00:48

t 01-Module 03-Lesson 05_Convolutional Neural Networks

03 Deep Learning A06 CNNs Have Taken Over-yNOHThuy2UU

00:19
Intro to CNNs-B61jxZ4rkMs

00:11
Color-Question-BdQccpMwk80

00:30
Statistical Invariance-0Hr5YwUUhr0

01:32
Convolutional Networks-ISHGyvsT0QY

03:04
Feature-Map-Sizes-Question-lp1NrLZnCUM

00:27
Feature-Map-Sizes-Solution-W4xtf8LTz1c

00:45
Convolutions Cont.-utOv-BKI_vo

00:37
Explore the Design Space-FG7M9tWH2nQ

02:16
1×1 Convolutions-Zmzgerm6SjA

01:10
Inception Module-SlTm03bEOxA

01:06

t 01-Module 03-Lesson 06_LeNet for Traffic Signs

LeNet Architecture-FQYrzmnbsMs

01:02
LeNet Data-eCP8o1BnhRA

01:37
LeNet Implementation-EA3f6zTbbo0

03:00
LeNet Training Pipeline-yof-J8Xktlk

01:52
LeNet Evaluation Pipeline-cibEsIOTE60

00:53
LeNet Model Training-9580p7ZRQVY

01:04
LeNet Testing-NHoyQSZNWA4

00:36
LeNet on AWS-1shr08mozc0

03:08
LeNet for Traffic Signs-VfJvSF087SI

03:41

t 01-Module 03-Lesson 07_Traffic Sign Classifier Project

t 01-Module 03-Lesson 08_Keras

t 01-Module 03-Lesson 09_Transfer Learning

Introduction-mMT_3k1LvNU

01:01
Bryan Catanzaro-CLIF_6QwlFo

01:16
L9 03 L GPUs-8eP2EpfBli0

01:30
Transfer Learning-pkCUxzJNtfI

00:53
Deep Learning History-AWWLT4QxKaM

01:19
Imagenet-pcNxBs7OAzA

01:12
Alexnet-X-QVsH27Mo4

00:58
Alexnet Today-AItZPkRHH_I

00:30
VGG-akFO1sH7Q_0

00:43
Empirics-VT8RENbE9Ck

01:28
GoogLeNet-sdT5f8n7IcI

02:16
ResNet-fDCgul26GGk

00:51
Outro-WOrM6qopj7A

00:34

t 01-Module 03-Lesson 10_Behavioral Cloning Project

03 Deep Learning A04 Simulation-K8ROKGMm-mc

00:44
Behavioral Cloning Project-YXs-IwG9ISg

01:15
01 – Running The Simulator-rKw8md-zVno

00:56
02 – Data Collection-kTJiHXJe_t4

01:01
03 – Data Visualization-_Gto6fQQWFI

02:06
04 – Training The Network-iYH4UvsPgOY

03:12
05 – Validating The Model-1UGOJGg-0dU

01:03
06 – Data Preprocessing-Oc7cLOS03PE

01:11
07 – LeNet Again-rVusn6F5i7s

00:45
09 – Data Augmentation-2oaB2_DhmF8

01:15
08 – Side Cameras-GumTdw9mjL0

01:45
10 – Image Cropping-SpPxyW-869U

00:47
11 – NVIDIA Architecture-6vVPHcgQkLg

00:51
12 – More Data Collection-cCZNlX3KLnY

00:16
13 – Generalizing Data Collection-SfHOdOHf4Zk

00:31

t 01-Module 04-Lesson 01_Camera Calibration

02 Computer Vision A01 The Challenges With Cameras-n2RSEjPn814

00:28
L21 Advanced Techniques For Lane Finding A02 L Welcome To Computer Vision-FRBWnuf1OIg

01:56
Overview-yN7u0qmJDhA

01:02
Getting Started, Camera Calibration-0zlyx5nL8Uo

01:06
Distortion-Bv3o0INBQIU

00:56
Pinhole Camera Model and Types of Distortion-FBHyHUN-A8c

02:50
Distortion-q3B71N6FrGw

00:56
Chessboards-lA-I22LtvD4

04:43
Lane Curvature-2UQ22uRybuU

01:07
Perspective Transform-JTesiOANhB0

02:48
Transforming a Stop Sign-OXILkkXXY8A

04:31

t 01-Module 04-Lesson 02_Gradients and Color Spaces

t 01-Module 04-Lesson 03_Advanced Techniques for Lane Finding

t 01-Module 04-Lesson 04_Advanced Lane Finding Project

t 01-Module 04-Lesson 05_Machine Learning and Stanley

Intro to Classifiers-u7XLk1Q9Efw

00:43
ML in The Google Self-Driving Car-lL16AQItG1g

02:12
Stanley Terrain Classification-MMllI8f5O5E

00:33
Speed Scatterplot Grade and Bumpiness-IMWsjjIeOrY

01:27
Speed Scatterplot Grade and Bumpiness-v2UbL0SOm9A

00:04
Speed Scatterplot 2-ijy0n1EjY0M

00:08
Speed Scatterplot 2-T4GbEVybNlY

00:05
Speed Scatterplot 3-4qJwfAWG_wQ

00:34
Speed Scatterplot 3-PaE5caOJ5kg

00:06
From Scatterplots to Predictions-dGS0SKu1ox0

00:33
From Scatterplots to Predictions-SuGzxfoye9s

00:07
From Scatterplots to Predictions 2-tkllhaHoko8

00:09
From Scatterplots to Predictions 2-vG3ahYyLHlQ

00:10
From Scatterplots to Decision Surfaces-DLCq1-kOGX0

00:20
From Scatterplots to Decision Surfaces-gbkORDbJM50

00:32
A Good Linear Decision Surface-sudTOiG-NJo

00:16
A Good Linear Decision Surface-z-cX1kYbC1w

00:47
Transition to Using Naive Bayes-2_dJXh1qqe0

00:17
NB Decision Boundary in Python-pauohSxuCVs

00:37
Getting Started With sklearn-olGPVtH7KGU

01:25
Gaussian NB Example-wpnDwiqTCJA

03:02
GaussianNB Deployment on Terrain Data-TcSnd3_hAy8

00:12
GaussianNB Deployment on Terrain Data-VBs6D4ggnYY

00:56
Calculating NB Accuracy–gJJmckPBAg

01:23
Calculating NB Accuracy-m989etSymQQ

00:44
Training and Testing Data-x2dmBUEKQIA

01:12
Naive Bayes Strengths and Weaknesses-nfbKTrufPOs

01:29
Congrats on Learning Naive Bayes-nQsYfzO7-00

00:20

t 01-Module 04-Lesson 06_Support Vector Machines

Welcome to SVM-gnAmmyQ_ZcQ

00:23
Separating Line-mzKPXz-Yhwk

00:50
Separating Line-NTm_mA4akP4

00:06
Choosing Between Separating Lines-ppSLADGROp8

00:04
Choosing Between Separating Lines-swoZxkrxIB0

00:19
Maximizing the Margin-otAraUuSrJo

00:34
Practice with Margins-ICUYxC-8d7o

00:41
Practice with Margins-l3zXhTxQiTs

00:09
SVMs and Tricky Data Distributions-wbCq7wm81BU

00:17
SVMs and Tricky Data Distributions-yD9C03vqNeI

00:43
SVM Response to Outliers-TEAGqUkQVdM

00:28
SVM Response to Outliers-w-czJptEyBk

00:41
SVM Outlier Practice-osn2fVnCVgQ

00:37
SVM Outlier Practice-WxAO6ByCvew

00:20
Handoff to Katie-GkqOdgZnkig

00:10
SVM in SKlearn-R7xQtQzkvTk

01:44
Coding Up the SVM-2ieszOqnpWs

00:51
Coding Up the SVM-CvTXyvw7QLc

01:22
Nonlinear SVMs-6UgInp_gf1w

00:56
Nonlinear Data-EllzeBecnkU

00:31
Nonlinear Data-PxE2bbG2Hkw

00:18
A New Feature-8TqVHRan4Fo

00:05
A New Feature-8xFV-I4VqZ0

01:19
Visualizing the New Feature-sAdM20gFi2M

01:09
Separating with the New Feature–_jNi_5zEEQ

00:35
Separating with the New Feature-9KAHkienFWk

00:09
Practice Making a New Feature-MXXTeWLXliY

01:09
Practice Making a New Feature-ygveMIhCtDg

00:39
Kernel Trick-3Xw6FKYP7e4

01:39
Playing Around with Kernel Choices-krV6r7HxmZU

02:17
Playing Around with Kernel Choices-sy_jiSEy-Nw

00:36
Kernel and Gamma-pH51jLfGXe0

01:20
Kernel and Gamma-znlTyocTgSc

00:24
SVM C Parameter-joTa_FeMZ2s

01:49
SVM C Parameter-WVg5-vxQDm8

00:35
SVM Gamma Parameter-m2a2K4lprQw

01:59
Overfitting-CxAxRCv9WoA

01:06
Overfitting-plx_F2BkwNQ

00:28
SVM Strengths and Weaknesses-U9-ZsbaaGAs

00:56

t 01-Module 04-Lesson 07_Decision Trees

Welcome To Decision Trees-5eAHVk1-Hz0

00:24
Linearly Separable Data-lCWGV6ZuXt0

01:16
Linearly Separable Data-YNfxSsQT78Y

00:06
Multiple Linear Questions-p_xPoBRJdtE

00:59
Multiple Linear Questions-t1Y-nzgI1L4

00:47
Constructing a Decision Tree First Split-GMe5JT2_oUE

00:27
Constructing a Decision Tree First Split-iZYv1WdWwQo

00:38
Constructing a Decision Tree 2nd Split-CIxvkVy1UBI

00:16
Constructing a Decision Tree 2nd Split-U2yZxIeG2t0

00:16
Class Labels After Second Split–3VPMBIwTtE

00:12
Class Labels After Second Split-A7KKnDmZBA0

00:10
Constructing A Decision TreeThird Split-1GCPKAYDPTg

00:50
Constructing A Decision TreeThird Split-RxySNoOmXnc

00:08
Coding A Decision Tree-cxV6OAxCfIQ

01:27
Coding A Decision Tree-YaZu4waSryo

01:52
(object Object)-i7pRvuVoWg0

00:14
(object Object)-sCZI5gWS6mg

00:28
Decision Tree Parameters-Is5T4alCCGQ

00:34
Decision Tree Parameters-jkJ4dbbpVCQ

02:18
Min Samples Split-Mt5TWGYacJs

00:29
Min Samples Split-xU84TShi7I4

00:34
Decision Tree Accuracy-1z5mVNdF1KA

00:09
Decision Tree Accuracy-EOLzooGccPc

00:58
Data Impurity and Entropy-Bd15qhUrKCI

01:22
Minimizing Impurity in Splitting-L6J6BRFgDiI

00:35
Minimizing Impurity in Splitting-lfZg7j5W7u8

00:36
Formula of Entropy-NHAatuG0T3Q

00:52
Entropy Calculation Part 1-JX3NN5zwL08

00:02
Entropy Calculation Part 1-K-rQ8KnmmH8

00:48
Entropy Calculation Part 2-3tzTP3e0Cjw

00:03
Entropy Calculation Part 2-GtiLFC7EgFE

00:05
Entropy Calculation Part 3-M2Sp-Y2a71c

00:13
Entropy Calculation Part 3-WmnGwUCW-Yc

00:07
Entropy Calculation Part 4-bhwb2v9rEdI

00:08
Entropy Calculation Part 4-V0FNwMKhIVM

00:11
Entropy Calculation Part 5-B_fHrMIzIgE

01:33
Entropy Calculation Part 5-ZSkYbBsFuOQ

00:06
Information Gain-KYieR9y-ue4

00:44
Information Gain Calculation Part 1-daVA3PI2E6o

00:29
Information Gain Calculation Part 1-erdekkpG-Do

00:23
Information Gain Calculation Part 2-4YP0K-5c310

00:08
Information Gain Calculation Part 2-t4qaavAslSw

00:07
Information Gain Calculation Part 3-s_-I8mbrfw0

00:06
Information Gain Calculation Part 3-yWPbe8onCeA

00:07
Information Gain Calculation Part 4-i6aCKjMeZPk

00:05
Information Gain Calculation Part 4-j0uDMc3Yrlo

00:07
Information Gain Calculation Part 5-3jfQlMLyH2o

01:11
Information Gain Calculation Part 5-4oOXVejgFGk

00:07
Information Gain Calculation Part 6-qnfVoUChRlQ

00:46
Information Gain Calculation Part 6-zqmrW9N9WGw

00:23
Information Gain Calculation Part 7-EDFp4wU5BMo

00:14
Information Gain Calculation Part 7-frzL4n6Y-vU

00:03
Information Gain Calculation Part 8-c7UjSq7Fmr8

00:08
Information Gain Calculation Part 8-F-xSYJ3y_pA

00:04
Information Gain Calculation Part 9-PDqyWzZCVBY

00:05
Information Gain Calculation Part 9-V-jzhJoeZj8

00:15
Information Gain Calculation Part 10-o75xNa_jwvg

01:08
Information Gain Calculation Part 10-XYHTuv2FpWQ

00:16
Tuning Criterion Parameter-V80QLNK5fFQ

00:56
Bias-Variance Dilemma-W5uUYnSHDhM

01:09
DT Strengths and Weaknesses-KGnhg76iRfI

01:17

t 01-Module 04-Lesson 08_Object Detection

Intro to Vehicle Tracking-BAe-zslg088

00:57
Intro to Arpan and Drew-zfWvntpxbK0

01:41
Object Detection Overview-zNBWOHycI0I

00:43
Features-u3NOabeuMjA

00:29
Color Features-JT4fDW7lsG8

00:58
Color Histogram Features-8ZvNANafMU8

01:12
Color Spaces-Adunl74VJIY

02:05
Gradient Features-cvGtDBu8ONQ

01:09
HOG Features-DNweoAqjwNQ

02:33
Combining Features-5tQx6J-VzsI

01:33
Build a Classifier-YUCFFNC7tw4

00:29
Labeled Data-H_i1ctez_Qw

01:34
Train a Classifier-EBEN6KLQm8A

01:46
Sliding Windows-HMtd9EQooCk

00:39
Multi-scale Windows-uiPNkPggWLE

00:40
False Positives-rihCsPhUSPk

01:01
Tracking Pipeline-ioaSZFCn3iI

00:42
Summary of Vehicle Detection and Tracking-3ceKmVDQfFQ

01:09
Traditional vs. Deep Learning Approach-_IFdaC0lWhI

01:35

t 01-Module 04-Lesson 10_The End

t 02-Module 01-Lesson 01_Welcome

t 02-Module 02-Lesson 01_Introduction and Sensors

t 02-Module 02-Lesson 02_Kalman Filters

Kalman Filter Introduction-2zmbIjHpkRM

00:56
Tracking Intro-BkjQzEyJWrE

01:56
Gaussian Intro-6IhtnM1e0IY

02:00
Variance Comparison-rczAG7meAY4

00:53
Variance Comparison-TGdMG81hXc8

00:24
Preferred Gaussian–9AVZ-N_gbM

00:34
Preferred Gaussian-sBsju-6nQWI

00:11
Evaluate Gaussian-4-0nBfsD4jo

00:14
Evaluate Gaussian-mQtjczyAxQs

00:38
Maximize Gaussian-2cD8T65E-jM

00:16
Maximize Gaussian-fRYtUP0P4Lg

00:52
Shifting the Mean-8c479K2UCZo

01:38
Shifting the Mean-HmcurWkA0fQ

00:24
Predicting the Peak-PsyqM704q2Y

00:36
Predicting the Peak-zc_GQiISQ3E

00:40
Parameter Update-d8UrbKKlGxI

01:54
Parameter Update-Lwn6FJgyyYI

00:30
Parameter Update 2-_AAkw_fynwc

00:57
Parameter Update 2-2BfisMbu86o

00:28
Separated Gaussians-fGcozDEwnwY

00:08
Separated Gaussians-QAqsIWVVX0Y

00:28
Separated Gaussians 2-0FmTokjoRgo

00:22
Separated Gaussians 2-edcfMK_bKXw

00:59
New Mean and Variance-SwxRWZaC1FM

00:37
New Mean and Variance-yo8jf0U4hlc

01:00
Gaussian Motion-X7YggdDnLaw

02:43
Gaussian Motion-xNPEjY4dsds

00:09
Predict Function-AMFig-sYGfM

00:13
Predict Function-DV2cX9W0tT8

00:28
Kalman Filter Code-3xBycKfnCOQ

01:33
Kalman Filter Code-X7cixvcogl8

03:47
Kalman Prediction-doyrdLJ6rJ4

01:24
Kalman Prediction-tSfmiuB9s2c

01:01
Kalman Filter Land-LXJ5jrvDuEk

02:37
Kalman Filter Prediciton-HTL5-0DDqE4

01:32
Kalman Filter Prediciton-SK3cnmu8BYU

00:27
Another Prediction-cUKlYjQEQGY

00:13
Another Prediction-JNDsm_Gzxi0

00:14
More Kalman Filters-hUnTg5v4tDU

03:57
Kalman Filter Design-KYEr4BXhD_E

03:07
Kalman Matrices-ade97UKqSIc

05:44
Kalman Matrices-LEuzK9X7_hM

04:05
Conclusion-6kFMxhlfHuI

00:39

t 02-Module 02-Lesson 03_C++ Checkpoint

t 02-Module 02-Lesson 04_Lidar and Radar Fusion with Kalman Filters in C++

04 Sensor Fusion A04 Kalman Filters In C++-Hsvzm7zDG_A

00:21
Introduction And Overview-G57ZvTBAUL8

01:11
Lesson Map-_u8Vk58VqxY

02:04
Refresh Estimation Problem-Uwq7_6slV_M

03:08
ND013 M3 L4 05 L Kalman Filter Equations In C++-ZG8Ya-mCGhI

01:48
Kalman Filter Equations In C++ Programming-KV4ZdUnOz9I

02:50
Kalman Filter Equations In C++ Programming-smRjTGQG2SY

00:42
State Prediction-_A0NRvmgo3w

01:32
Process Covariance Matrix-iFcIiqRGaws

02:56
Laser Measurements-drbV05qKV8w

00:53
Programming Assignment-gTEQHV_1E2k

04:05
Programming Assignment Solution-udsB-13ntY8

02:11
Radar Measurements-LOz9AaHvB8M

03:27
Extended Kalman Filter-nMUd_esBMM8

02:48
Jacobian Matrix-FeE5cRlMZqU

02:06
Jacobian Matrix-pRhuwlMhG3o

00:29
EKF Algortihm Generalization-co0ZczAuwdM

00:57
Sensor Fusion General Processing Flow-dcTY4vRg5vo

01:16
Evaluating The Performance-1HieeV8IUv8

01:52
Evaluating The Performance-1iVBYQ_KWXk

00:32
Outro-k5VhLE0OoOM

00:43

t 02-Module 02-Lesson 05_Extended Kalman Filter Project

t 02-Module 02-Lesson 06_Unscented Kalman Filters

Intro-HbPxeJ3onmI

01:23
CTRV-g72HXEcSQHU

00:27
State Vector Introduction-hLVz0YOhntA

00:53
CTRV-o2HVZFSH1Fs

01:45
CTRV Integral Last 3-dcR9RtwJ6yk

01:06
CTRV Integral Position-9E6K4Aw_MaI

01:03
CTRV Zero Yaw Rate-8gAsx7OAH6c

00:52
CTRV Process Noise Effect Last 3-DUm8e7K8qZ8

00:45
CTRV Process Noise Vector-Qr99RXys-G0

01:09
13 Q CTRV Process Noise Position-DJ_K1udemNk

01:50
UKF Process Chain-sU7ifLgxxas

01:03
UKF What’s the Problem-OFb47Lu9JfM

03:38
UKF Basic Unscented Transformation-r594P0XjKa4

00:49
UKF Basics Unscented Transformation-8jbckHQDl4A

01:53
19 UKF Generate Sigma Points-t7YJJpEzTX4

04:13
Assignment Sigma Point Generation-TIc3n-cxTqc

02:04
22 L UKF Augmentation-G-kdutCM1RQ

04:00
Assignment Augmentation-5p-PqtxQeM8

00:46
Sigma Point Prediction-zeMy0dth3yI

01:17
Assignment Sigma Point Prediction-RQvnRpSPUak

01:07
26 L Predict Mean And Covar-6DELFN7Fz4c

01:42
Assignement Predicted Mean And Covariance-0vl_wfDpVec

00:26
Measurement Prediction-qDX8nL_OT60

03:24
Assignment Predict Radar Measurement-GYQeizoj09E

00:40
UKF Update-pJ5XauGNclI

01:39
Assignment UKF Update-f36o4sCEQvY

01:07
Parameters And Consistency-S4fX3X_9oik

06:19
What to Expect from the Project-WAt_g6HgYvs

03:48
UKF Story Time-gFdT8W1fmf8

01:12
Outro-G3soGuQeHGU

01:48

t 02-Module 02-Lesson 07_Unscented Kalman Filter Project

t 02-Module 03-Lesson 01_Introduction to Localization

t 02-Module 03-Lesson 02_Localization Overview

Introduction-Uqt_pRbR8rI

03:32
Localization-31xZhj2uPr4

02:10
Total Probability-n1EacrqyCs8

04:34
Uniform Probability Quiz-6tV5NY1HoNA

00:36
Uniform Probability Quiz-IZC33Tmy8Lo

00:09
Uniform Distribution-_sAkAALHyEg

00:05
Uniform Distribution-ysebYA6tDZ4

00:19
Generalized Uniform Distribution-e21oU80gwWc

00:22
Generalized Uniform Distribution-nsSvTTA0p8E

00:25
Probability After Sense-dEiQObhi2J4

00:14
Probability After Sense-UFcTLCttNRI

01:38
Compute Sum-qa9B4r5m8wM

00:23
Compute Sum-WgX17_mmc1c

00:03
Normalize Distribution-SW_wvez0izo

00:50
Normalize Distribution-Uc_rHR6U70U

00:21
pHit and pMiss-FnhHQht4vDo

00:16
pHit and pMiss-wOfAyDvun5w

00:19
Sum of Probabilities-6c0XvswnGm0

00:08
Sum of Probabilities-z0oijOqN8K8

00:09
Sense Function-eIjyrQpDogg

01:20
Sense Function-Y5iFxWRTw1c

00:46
Normalized Sense Function-GqWszyHTYas

00:13
Normalized Sense Function-UX3W8TUKbJ0

00:27
Test Sense Function-F8AHaaJVmkw

00:22
Test Sense Function-Lf2DYUCsUH4

00:51
Multiple Measurements–3qTapGGa-8

00:42
Multiple Measurements-gDO4sF8gR9k

00:26
Exact Motion-1mL6CtD3rAM

00:37
Exact Motion-Iky7rJXQU_4

00:25
Move Function-TnFq6hufsYs

01:04
Move Function-wfjE0mVADIk

00:57
Inexact Motion 1-C3f-T9_GTpw

01:40
Inexact Motion 1-mGWGhgZG_jM

00:12
Inexact Motion 2-gZbPZLFKS68

00:17
Inexact Motion 2-jR7FERpsqe4

01:03
Inexact Motion 3-7T1Rr7KLgdM

01:21
Inexact Motion 3-BldUOLB2U1Y

00:09
Inexact Move Function-68Kao9dkIKA

00:14
Inexact Move Function-QCnPJcNprEU

00:46
Limit Distribution Quiz-kfPWiMsnWFI

01:00
Limit Distribution Quiz-SXSafquSoW8

01:55
Move Twice-oqlgQa1IdcY

00:12
Move Twice-sKiumVTdpgY

00:14
Move 1000-nYt9b_pNvEE

00:12
Move 1000-x2o1g3J-1nw

00:06
Sense and Move-1s2dRczcu1A

01:08
Sense and Move-K8g3Hss8Q1A

01:48
Sense and Move 2–wT7h9Gdm_8

00:12
Sense and Move 2-rmWL_3r8MKo

01:56
Localization Summary-MVbo4OAgQCc

01:06
Formal Definition of Probability 1–F2gJXWbN6s

00:26
Formal Definition of Probability 1-OQ2JS2wQzrs

00:05
Formal Definition of Probability 2-PE-k3PGXeLY

00:06
Formal Definition of Probability 2-uw51WQDqXAI

00:03
Formal Definition of Probability 3-oDPbdGXH5nE

00:10
Formal Definition of Probability 3-TF6AWXSlOcY

00:12
Bayes’ Rule-sA5wv56qYc0

03:03
Cancer Test-OgC5M2XdIac

01:22
Cancer Test-SZ6Jg1wS604

01:13
Theorem of Total Probability-byZ-BzbQA5M

02:00
Coin Flip Quiz-ASUXN9Ay35M

00:58
Coin Flip Quiz-hzDsYZ61D5M

00:32
Two Coin Quiz-_AhoOd8YUK0

01:11
Two Coin Quiz-2PZHPjyYnMg

00:39

t 02-Module 03-Lesson 03_Markov Localization

05 Localization A02 A Return To Bayes Ruls-7FgzbyeJCp8

00:25
Markov Location Lesson Overview-rSj5lpzliQg

00:37
ND013 M4 L3 02 L Localization Posterior-WCva9DtGgGA

02:06
03 L Explain Localization Posterior V2-lGpIgbA5ZdA

03:08
Quiz How Much Data-wzcFHAf-9lo

00:36
03.5 S How Much Data-PQV6gWuyVOs

00:44
Derivation Outline-coHodx-I56U

00:57
Apply Bayes Rule With Additional Conditions-RsHS2o3zjcw

02:30
Explain Bayes Rule And Apply Law Of Total Probability-p2qfHa9G7_k

01:58
Explain Law Of Total Probability And Markov Assumption-9hGU7s5m8c0

01:59
Explain Markov Assumption For Motion Model-YFLAFptKU5E

05:02
Explain Recursive Structure-d0GrWJeVFjU

02:38
Implementation Details For Motion Model-O47bOcJm1eE

01:11
ND013 M4 L3 15.5 Q Noise In Motion Model-zRbT36RTlhs

00:42
Noise In Motion Model Solution-zJ9NWz7IlOM

00:34
Observation Model Intro-SDM1aVqRBCk

00:59
Markov Assumption For Observation Model-dyDjINdrIz0

03:27
Finalize The Bayes Localization Filter-teVw2J-_6ZE

01:35
Bayes Filter Theory Summary-lMyu2-PZGuk

01:11
Conclusion-3npZxfdrOpY

00:28

t 02-Module 03-Lesson 04_Motion Models

t 02-Module 03-Lesson 05_Particle Filters

Field Trip-2ocy_7PJtfA

02:02
State Space-G7nvigL0aDw

01:01
State Space-oyw7WEHMvVY

00:11
Belief Modality-5vdbYPc7tWw

00:23
Belief Modality-NhKyyhNl70A

00:22
Efficiency-7CNEY8lRrGE

00:34
Efficiency-rA8ZpMR6yXM

00:54
Exact or Approximate-1N3_RnTDFqU

00:35
Exact or Approximate-WKlm2aO2QGY

00:18
Particle Filters-4S-sx5_cmLU

03:46
Using Robot Class-1hgVZtRIjFU

02:00
Robot Class Details-ZFqEh8JylvI

00:42
Moving Robot-_37pf6lV15s

00:29
Moving Robot-SFcHsK2SWrI

01:07
Add Noise-ajOKsQLxoJI

00:36
Add Noise-FQEeI3qzaOM

00:19
Robot World-qq5h-Xw4DGg

00:37
Creating Particles-dH6uzx78lBA

01:30
Creating Particles-JNI9O9FjfDQ

00:49
Robot Particles–gNoDMlRwyc

00:53
Robot Particles–HQf6pkcebQ

00:42
Importance Weight-VJvBzdTPlAQ

00:58
Importance Weight-xP9PrSTJPz0

05:36
Resampling-FjRX_i3SsJA

00:35
Resampling-zlCJQmxvrkE

02:31
Never Sampled 1-8ffPkDiDioI

00:13
Never Sampled 1-MhhM1uh0-3w

00:11
Never Sampled 2-i457B5Iyg-8

00:12
Never Sampled 2-q95KMAIqDDY

00:15
Never Sampled 3-hcoKwWBvB6Y

01:22
Never Sampled 3-Z1oQl-1cUeE

00:18
New Particle-AROtzVxDDx4

00:58
New Particle-LJXbHoq5EZk

00:41
Resampling Wheel-aHLslaWO-AQ

03:22
Resampling Wheel-wNQVo6uOgYA

03:12
Orientation 1–lq0uzHd9T0

00:42
Orientation 1-cupiUHaKvdI

00:18
Orientation 2-17FoJwLiQkg

00:04
Orientation 2-Ex0su1DnIuw

01:00
Error-3kOrzhYCXz8

01:26
Error-UAdcKWLi9G8

01:47
You and Sebastian-gTMe0E6SM_M

00:48
Filters-bjZy-RVms_8

02:53
Filters-d_DXbkU7iPY

00:32
2012-QgOUu2sUDzg

01:58

t 02-Module 03-Lesson 06_Implementation of a Particle Filter

05 Localization A04 Particle Filters In C++-zL6dEu1jrH8

00:08
Lesson Introduction-_VjhAIChVcI

00:48
Pseudocode-JNm1fnWj5To

00:55
Initialization-agPdu0c5_GM

02:05
Prediction Step-kNthLZTHDIM

00:42
Data Association- Nearest Neighbor-nG_pFGT-fuo

01:59
Nearest Neighbor Advantages And Disadvantages-snXId_LyzXs

03:34
Update Step-1Uq2QZKz3aI

04:05
Calculating Error-HiRrJYZr-0I

01:30
ND013 M4 L6 Converting Landmark Observations-BrQfVd4JXpg

03:53
Explanation of Project Code-3VRp4chnPE4

05:19

t 02-Module 03-Lesson 07_Kidnapped Vehicle Project

t 02-Module 04-Lesson 01_PID Control

Intro-CelGYr2DgpI

00:48
PID Control – Artificial Intelligence for Robotics–8w0prceask

01:14
Proportional Control – Artificial Intelligence for Robotics-gGo-gSFqYqg

00:34
Implement P Controller – Artificial Intelligence for Robotics-OrJgrTc5d04

02:08
Implement P Controller Solution – Artificial Intelligence for Robotics-wvdFPAOCb64

00:25
Oscillations – Artificial Intelligence for Robotics-CO3zjkxBaIc

00:18
PD Controller – Artificial Intelligence for Robotics-kVYy2kjZjhA

02:34
PD Controller Solution – Artificial Intelligence for Robotics-YgomQgfFlTQ

00:27
Systematic Bias – Artificial Intelligence for Robotics-1wxFEcqq3_c

00:50
Is PD Enough – Artificial Intelligence for Robotics-gDbpwPdStlY

00:11
PID Implementation – Artificial Intelligence for Robotics-Ag8H3Iit9j4

01:43
PID Implementation Solution – Artificial Intelligence for Robotics-dgZnqCfyCoA

01:30
Twiddle – Artificial Intelligence for Robotics-2uQ2BSzDvXs

03:19
Parameter Optimization – Artificial Intelligence for Robotics-A2b3F5Ae53Y

02:10
Parameter Optimization Solution – Artificial Intelligence for Robotics-YQ5Pa-OKQm0

02:33
Outro-zF_MUxjCl04

00:16

t 02-Module 04-Lesson 02_PID Controller Project

t 02-Module 04-Lesson 03_Vehicle Models

Vehicle Models Intro-qkT7Sr8HHBw

00:39
State-6vFczwAYjsU

00:27
04 L Building A Kinematic Model–Nnk8n81zr4

00:44
Following Trajectories-sOSHaAf_7b8

00:30
Errors-qtg_HiqoGHY

00:32
Dynamic Models – Part 1 Forces-KRN7GVJkFnU

00:32
Dynamic Models – Part 2 Slip Angle-oDusBbn820k

00:32
Dynamic Models – Part 3 Slip Ratio-kSqOJDwRFVc

00:21
Dynamic Models – Part 4 Tire Models-OFIL0yqsV7o

00:33
15 L Actuator Constraints-EwcDwdM1msg

00:51
Vehicle Models Outro-DqKw_m3Uxr0

00:33

t 02-Module 04-Lesson 04_Model Predictive Control

t 02-Module 04-Lesson 06_The End

t 02-Module 05-Lesson 01_Geometry and Trigonometry Refresher

Trig Preface-DCX6mK9cre0

00:44
L3 01 V2-a_eHxfy5tnI

00:46
Nd113 C6 L3 04 L Moving At An Angle V2-2KDq_ZzN3Mk

00:59
Nd113 C6 L3 045 L Moving At An Angle Part2 V2-iI6zCp0RegM

00:52
Nd113 C6 L3 05 L Moving At 53 Degrees V2-VmoknN6xLKs

00:48
Nd113 C6 L3 055 L Moving At 53 Degrees Solution V2-Y_3M6eeYbd8

00:21
Power of Trigonometry-yLMTfBq_I3k

00:54
Nd113 C6 L3 08 L Opposite Adjacent Hypotenuse V2-hJ-7qj9iHWo

00:50
Nd113 C6 L3 09 L Trigonometric Ratios V2-wquwvrT9g_U

01:03
Nd113 C6 L3 095 L Trigonometric Ratios Solution V2-c5iuVhWCOzc

00:14
Trigonometry And Vehicle Motion-WY3T-9GHI_0

00:34
Solving Trig Problems Part1-qI4i845d7Qg

01:38
Conclusion-gMbDqd4ItiU

00:39

t 03-Module 01-Lesson 01_Welcome

t 03-Module 02-Lesson 01_Search

06 Path Planning A01 Motion Planning-JlBs1RxKvHE

01:02
01 L Intro Group-f7lpxAQDxVI

01:15
Motion Planning-KHAu5A_flcQ

02:06
Compute Cost-7-yOaHVeATk

00:25
Compute Cost-dBA94pR6JYw

00:48
Compute Cost 2-n9_th4V4qE4

00:38
Compute Cost 2-OXIESpN0KaE

00:22
Optimal Path-Exl_kCyUc8U

00:47
Optimal Path-wLz-nF2CrHc

00:52
Optimal Path 2-GvCSZOR3hLQ

00:07
Optimal Path 2-qFswCrEUZSM

00:22
Maze-ge_-o0RfrgM

00:32
Maze-yVh0lVlerWs

00:16
Maze 2-aBUxPyEDOWw

00:10
Maze 2-YwAyqkznxa0

01:11
First Search Program – Artificial Intelligence for Robotics-TPIFP4E7DVo

06:55
First Search Program Solution – Artificial Intelligence for Robotics-cl8Kdkr4Gbg

05:04
Expansion Grid – Artificial Intelligence for Robotics-1l7bWfz8sJw

01:24
Expansion Grid Solution – Artificial Intelligence for Robotics-pH6sDfBalaw

00:43
Print Path-6UJFZf40aBg

01:41
Print Path-CyQ2gl-9W4o

03:00
A-lxCCtgHk5Vs

08:27
Implement A-SSyvcCZKlqo

03:54
Implement A-V0Ppaw5G2Pg

00:31
A in Action-qXZt-B7iUyw

03:33
Dynamic Programming-r2bPY2s9wII

03:14
Computing Value-ebFQqd7Uhek

00:13
Computing Value-Sn-ZUbZdOn8

02:28
Computing Value 2-t2aT92C2ruA

00:12
Computing Value 2-yTV3JPJk1kE

00:03
Value Program-FdT1g_Bzjm0

02:41
Value Program-RXpuBRA-cpo

01:21
Optimum Policy-7kllZxX-Nso

01:05
Optimum Policy-MMDcirk9QPM

01:00
Left Turn Policy-bQA2ELDNmmg

03:57
Left Turn Policy-rH5DKpwYQLY

03:25
Planning Conclusion-M7ZJ74RVHqo

02:08

t 03-Module 02-Lesson 02_Prediction

01 L Introduction And Overview-aHmVFZ6hMjc

04:52
04 L Model Vs Data Driven Approaches-ehfA_NC7Ka4

02:17
06 L Data Driven Example Trajectory Clustering-jbFeQ9P2V9A

03:06
07 L TrajectoryClustering2 – Online Prediction-UPiED4soM4w

02:34
08 L ThinkingAboutModelBasedApproaches-2JHmXN4AKNY

03:19
09 L ProcessModels-VcRDsKBn7tc

04:36
11 L MultimodalEstimationApproaches-u1Tmt0Qdlgk

02:55
13 L Overview Of Hybrid Approaches-yCRvxI5wJS0

01:19
14 L IntroToNaiveBayes-AkrC_WP1MWk

02:56
Mahni Prediction Outro-hRozwCcoocY

00:59

t 03-Module 02-Lesson 03_Behavior Planning

06 Path Planning A03 Where To–9ZrXlxPH4L4

00:31
01 L Lesson Outline-qyH-1BMCiUY

01:43
03 L The Behavior Problem-5t-oVAZagT8

02:22
04 L Finite State Machine-NERRPjU08NU

01:25
05 L Formalizing FSMs-sEZn3iZgOaI

01:40
07 L StatesForASelfDrivingCar-zoN0-IPe0I4

02:33
08 L StatesForASelfDrivingCarSolution-QXU6ptbxfyo

05:03
09 Q InputsToTransitionFunctions-8jStt2d_SYc

00:32
09 S InputsToTransitionFunctions-AjMSl8zR-P0

00:07
12 L CreateACostFunctionSpeedPenalty-wGRDT2wTnn8

02:32
14 L CostFunctionDesignWeightTweaking-NK6SP-r4dGs

05:57
16 L SchedulingComputeTime-N6AlIUczqRM

01:50
Toby Ben Outro-Hzk5-lezrJk

00:26

t 03-Module 02-Lesson 04_Trajectory Generation

06 Path Planning A04 From Behavior To Trajectory-7yo_HJ1_J9Q

00:34
01 L Lesson Overview-BU4oChjLv7A

00:51
02 L The Motion Planning Problem-daGIOru4Bi4

02:10
03 L Properties Of Motion Planning Algorithsm-lNpD43L0qvw

01:00
Types of Motion Planning Algorithms-6-K1aLTEvk8

03:32
A – Artificial Intelligence for Robotics-lxCCtgHk5Vs

08:27
06 L A- Reminder Solution-HtQjw7qr2-o

01:45
Hybrid A Introduction-NuurQejBk0o

02:58
09 L Hybrid A- Tradeoffs Solution-6yAdF5u2B04

00:58
10 L Hybrid A- In Practice-Mkz_WjyRzag

05:32
15 L EnvironmentClassification-4NOvHff7WFQ

01:09
16 L Frenet Reminder-u3TYp-hDojk

01:23
17 L The Need For T-4WsqXkB8zqQ

03:22
18 L S D And T-SD1iyzgFf8s

03:50
20 L Structures Trajectory Generation Overview-N2cwqKR63x8

00:49
21 L TrajectoriesWithBoundaryConditions-p73Jma9nW-Q

03:21
22 Jerk Minimizing Trajectories-pomDFkzy2bk

04:08
23 L DerivationOverview-TuVp_HhQq7A

02:00
25 L How Polynomial Trajectory Generation Works-5ZzYOqYZZ3I

01:11
27 L WhatShouldBeCheckedSolution-E2TBXjyYb_Y

00:48
29 L Implementing Feasibility-8tD8Os9_gKc

02:09
31 L PuttingItAllTogether-UhrmXmnKhQE

04:23
35 L Conclusion-R56iixkZvEE

00:44

t 03-Module 02-Lesson 05_Path Planning Project

t 03-Module 03-Lesson 02_Fully Convolutional Networks

t 03-Module 03-Lesson 03_Scene Understanding

t 03-Module 03-Lesson 04_Inference Performance

Intro-dK5Yn3sq5RM

00:24
Why Bother With Performance-pCg0q8qmgsk

00:38
L3 03 L Semantic Segmentation Revisited-xFcI26kLtiY

00:35
P1-Y2ggt6l7PTo

00:57
P2-Yr_rRyEOTnM

01:05
P3-yncq5HhDTV4

01:58
Fusion-JOksFH3vQgk

01:39
Reducing Precision-bZPG5I_igR8

01:41
Quantization Conversion-0kbFQerI86k

00:37
Outro-jMSB5_P_fss

00:21

t 03-Module 03-Lesson 05_Semantic Segmentation Project

t 03-Module 04-Lesson 02_Introduction to Functional Safety

L1 01 L Introduction To Module-cHadgZtuDZA

01:40
L1 02 L Introduction To Lesson-jinuK2-qPh4

01:08
What Is Safety-o1mdbufGaq8

01:18
What Is Functional Safety-NeUXHSv5qz8

01:19
Introduction To Identify Hazards-ZzgEw7WQgTs

01:17
L1 Sebastian-lw3O5Me6FRw

02:20
Introduction To Evaluating Risks-3N-6YK_pPzA

02:26
L1 17 Reducing Risk With System Engineering-tFxkJKcujhQ

03:26
L1 19 Introduction To Iso26262-bjvpmBOG60Q

02:01
Lesson Summary-c_PnlhHye3w

00:43
Sebastian Discusses Risk-nuDIfITdhSc

00:53

t 03-Module 04-Lesson 03_Functional Safety Safety Plan

t 03-Module 04-Lesson 04_Functional Safety Hazard Analysis and Risk Assessment

L3 01 Introduction- Item Definition And HARA-6rojuFZJ4Os

00:57
L3 04 L Introduction To HARA-Qxui0XShsbE

01:28
L3 Sebastian-sGbjIAiDEUs

01:06
05 HARA Situational Analysis-o9iBiz7IQ_k

02:29
L3 09 L HARA Identification Of Hazards-LXp7ScZaKp4

01:48
L3 14 HARA Risk Assessment, Severity, Exposure, Controllability-44NYK53gOAM

01:50
L3 14 Hara Risk Part 2–3EXJjR6fbk

00:53
L3 14 L Hara Risk Part 3-buFLRLfwSWw

00:59
L3 18 L HARA ASIL Levels-l7vx-w06fZw

01:54
L3 20 L HARA Safety Goals-lMT1EB5cZR8

00:50
L3 25 L HARA Outro-T0Xxu-Xh4J0

00:53

t 03-Module 04-Lesson 05_Functional Safety Functional Safety Concept

L4 01 L Introduction-srUVh4mBvws

00:35
L4 03 Functional Safety Requirements-9z5YqMYH7mY

01:17
L4 06 Allocation Of Requirements To Architecture-cDkQLZ3PJqM

02:18
Architecture Refinement-SK85D4cvwXo

01:11
L4 10 Function Safety Requirements And ASIL Inheritance-oeKSXaP7Lxg

01:36
L4 13 ASIL Decomposition-dphScT1QTNY

02:26
L4 15 Fault Tolerant Time Interval-o4PzRfVN_to

02:00
L4 18 Warning And Degradation Concept-khNhy3IwKa0

01:24
L4 Sebastian-0L2DG60xWy8

01:32
Summary Functional Safety Concept-NVCU1mOxnAo

00:39

t 03-Module 04-Lesson 06_Functional Safety Technical Safety Concept

t 03-Module 04-Lesson 07_Functional Safety at the Software and Hardware Levels

L6 01 L Intro-urgjYwIY3hs

00:38
L6 04 Hardware Failure Metrics-wlCVuAJj1xk

00:55
L6 06 Programming Languages-KpAhkXNan7Y

01:12
What Is MATLAB SelfDriving-6J3Ho1ZrHm0

01:39
L6 09 Software Safety Requirments Architecture Testing And Intergration-OJMGRtJciNI

01:39
L6 11-12 Software Safety Robustness And Quality-RQEnvtti3sM

02:05
L6 13 Freedom From Interference Spatial-HraIGQSxsQ0

01:29
L6 15 Freedom From Interference Temporal-ChGZCPXko7M

01:43
L6 17 Freedom From Interference Temporal Part 2-wRbGk0SwWNQ

01:28
L6 Sebastian-AD47B4rAKyY

02:10
L6 18 Freedom From Interference Communication-J2T842SLPgs

01:21
L6 20 System Architecture Safety Design Patterns-9q8WAW-g8jE

01:23
L6 22 L Lesson Outro-zSyoNGrZZ0k

00:47
L6 24 L Module Outro-QT-4yIV9dEM

00:26

t 03-Module 04-Lesson 08_Elective Project Functional Safety

t 03-Module 05-Lesson 01_Autonomous Vehicle Architecture

t 03-Module 05-Lesson 02_Introduction to ROS

08 System Integration A03 Communication Between Systems-a9hYIvymlkQ

00:25
L2 01 L ROS And Carla–G-kEnQWKgY

00:41
Welcome to ROS Essentials-rvebtwi46SI

00:44
Build Robots with ROS-7eaz0qW7y_I

02:05
Brief History of ROS-Cw-FEyqU2NI

00:51
Nodes And Topics-t5xWx5Zgmk0

02:35
Message Passing-IpNp13F-TgQ

01:18
ROS Services-EXYmvpcOnCc

01:27
Compute Graph-dWc4ktFohNg

01:10
Turtlesim Overview-GcAqAdMQXvM

01:10
Sourcing The ROS Environment-6cHlu-KVi98

01:24
Run Turtlesim-hCkE973oY9o

02:21
Turtlesim Comms List Active Nodes-J_5JTUi7sQQ

01:00
Turtlesim Comms Topics-46YAnfvhTMc

00:53
Turtlesim Comms Get Info-Y6rMQreuOL4

00:55
Turtlesim Comms Message Information-f89-UgEb8Y0

02:46
Turtlesim Comms Echo Messages-HNA7eKhYcyA

02:24
Recap-7WOQ89HYhxA

00:50

t 03-Module 05-Lesson 03_Packages Catkin Workspaces

t 03-Module 05-Lesson 04_Writing ROS Nodes

t 03-Module 05-Lesson 05_System Integration Project

t 03-Module 06-Lesson 01_Completing the Program

t 04-Module 01-Lesson 01_Conduct a Job Search

t 04-Module 02-Lesson 01_Refine Your Entry-Level Resume

t 04-Module 03-Lesson 01_Craft Your Cover Letter

t 05-Module 01-Lesson 01_Develop Your Personal Brand

t 05-Module 02-Lesson 01_GitHub Profile Review

Introduction-Vnj2VNQROtI

00:54
GitHub profile important items-prvPVTjVkwQ

01:35
Good GitHub repository-qBi8Q1EJdfQ

01:10
Interview with Art – Part 1-ClLYamtaO-Q

02:11
Identify fixes for example “bad” profile-AF07y1oAim0

00:12
Identify fixes for example “bad” profile-ncFtwW5urHk

00:51
Quick Fixes-Lb9e2KemR6I

01:11
Quick Fixes #2-It6AEuSDQw0

00:13
Writing READMEs with Walter-DQEfT2Zq5_o

00:40
Interview with Art – Part 2-Vvzl2J5K7-Y

01:19
Reflect on your commit messages-_0AHmKkfjTo

00:16
Participating in open source projects-OxL-gMTizUA

00:15
Interview with Art – Part 3-M6PKr3S1rPg

02:30
Participating in open source projects 2-elZCLxVvJrY

00:54
Starring interesting repositories-U3FUxkm1MxI

00:13
Starring interesting repositories-ZwMY5rAAd7Q

00:19
Outro-dps7Ti6Lado

00:26

t 06-Module 01-Lesson 01_Introduction and Efficiency

t 06-Module 01-Lesson 02_List-Based Collections

t 06-Module 01-Lesson 03_Searching and Sorting

Binary Search-0VN5iwEyq4c

01:28
Efficiency of Binary Search-7WbRB7dSyvc

08:44
Recursion-_aI2Jch6Epk

06:51
Intro to Sorting-Z6yuIen71zM

01:57
Bubble Sort-h_osLG3GmjE

02:32
Efficiency of Bubble Sort-KddkHygi7is

02:25
Merge Sort-K916wfSzKxE

04:16
Efficiency of Merge Sort-HKiK5Y-YSkk

04:58
Quick Sort-kUon6854joI

03:22
Efficiency of Quick Sort-aMb5GHPGQ1U

02:52

t 06-Module 01-Lesson 04_Maps and Hashing

t 06-Module 01-Lesson 05_Trees

Trees-PXie7f22v2Q

00:39
Tree Basics-oaxLPzaXRDc

01:27
Tree Terminology-mPUsDUR_sj8

01:42
Tree Traversal-KZOdmzypynw

01:48
Depth-First Traversals-wp5ohHFTieM

02:35
Search and Delete-KbL-HK3ztX8

01:26
Insert-j6PkPa2ZHWg

02:10
Binary Search Trees-7-ZQrugO-Yc

00:39
BSTs-abRNGLhGUmE

01:51
BST Complications-pcB0wV7myy4

00:54
Heaps-M3B0UJWS_ag

02:14
Heapify-CAbDbiCfERY

01:18
Heap Implementation-2LAdml6_pDY

01:37
Self-Balancing Trees-EHI548K3jiw

01:44
Red-Black Trees – Insertion-dIuWLtWnkgs

01:49
Tree Rotations-O5Yl-m0YbVA

01:49

t 06-Module 01-Lesson 06_Graphs

Graph Introduction-DFR8F2Q9lgo

00:46
What Is a Graph-p-_DFOyEMV8

02:40
Directions and Cycles-lF0vUktQDPo

01:58
Connectivity-4x6u2KtNDg4

01:19
Graph Representations-uw9u6dtl0WA

01:56
Adjacency Matrices-FsFhoTALA1c

01:47
Graph Traversal-Dkt-XxHZaZE

00:45
DFS-BC8jEidd2EQ

02:23
BFS-pol4kGNlvJA

01:36
Eulerian Path-zS34kHSo7fs

02:31

t 06-Module 01-Lesson 07_Case Studies in Algorithms

t 06-Module 01-Lesson 08_Technical Interviewing Techniques

Interview Introduction-dRsHYt1Lddc

01:16
Clarifying the Question-XvvKBmKC_84

01:37
Confirming Inputs-8lPTOG1yLsg

00:47
Test Cases-7CNatJ7PqZ4

00:53
Brainstorming-LJFYhMDCCsU

03:05
Runtime Analysis-8bI9OgOB2qI

00:40
Coding-zhQYREUI8Z0

10:14
Debugging-Bz1tlvkql9Q

03:06
Interview Wrap-Up-sz4Ekcu9a_Q

00:42

About Course

Deep Learning Specialization: Neural Networks and Deep Learning – Free Online Course

What Will You Learn?

Course Content

t 01-Module 01-Lesson 01_Welcome

A Message from the Professor

Welcome-jHA__A61nqc

Meet Your Instructors-QiflJFVOt18

Overview-RZ5iolr4RGs

What Projects Will You Build-JGpXenoW0dk

Meet the Careers Team-cuKecPpZ7PM

The Great Robot Race-saVZ_X9GfIM

t 01-Module 01-Lesson 03_Computer Vision Fundamentals

01 Introduction A01 Power Of Cameras-lCPWJEEzUeo

lane line intro-aIkAcXVxf2w

color selection-bNOWJ9wdmhk

region select-ngN9Cr-QfiI

Computer Vision-wxQhfSdxjKU

canny and gradient-LQM–KPJjD0

Canny Edge Detection-Av2GsgQWX8I

Hough Intro-JFwj5UtKmPY

Hough Quiz 5-upKjISd3aBk

sine Hough-XQf7FOhwOVk

t 01-Module 01-Lesson 04_Finding Lane Lines Project

01 Introduction A02 Building A Portfolio-q8ZysjEREiM

Outro to Project-LatP7XUPgIE

t 01-Module 02-Lesson 01_Career Services Available to You

Working at Mercedes-Benz-Z_hi4djW5aw

Working at NVIDIA-C6Rt9lxMqHs

Working at Uber ATG-V23NZzX0efY

t 01-Module 03-Lesson 01_Introduction to Neural Networks

03 Deep Learning A01 Neural Network Intuition-UKEIHK5IifI

Module Introduction-uyLRFMI4HkA

ND013 01 L Intro To Neural Networks-UIycORUrPww

DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q

Solution Housing Prices-uhdTulw9-Nc

Classsification Example-Dh625piH7Z0

Classification Example-46PywnGa_cQ

Linear Boundaries-X-uMlsBi07k

09 Higher Dimensions-eBHunImDmWw

DL 06 Perceptron Definition Fix V2-hImSxZyRiOw

Why Neural Networks-zAkzOZntK6Y

AND And OR Perceptrons-45K5N0P9wJk

XOR Perceptron-TF83GfjYLdw

07 Perceptron Algorithm Trick-lif_qPmXvWA

DL 10 S Perceptron Algorithm-fATmrG2hQzI

Perceptron Algorithm–zhTROHtscQ

Perceptron Agorithm Pseudocode-p8Q3yu9YqYk

Non-Linear Regions-B8UrWnHh1Wc

Error Functions-YfUUunxWIJw

Error Functions-jfKShxGAbok

Discrete vs Continuous-rdP-RPDFkl0

Discrete vs. Continuous-Rm2KxFaPiJg

DL 18 Q Softmax V2-RC_A9Tu99y4

DL 18 S Softmax-n8S-v_LCTms

Quiz – Softmax-NNoezNnAMTY

One-Hot Encoding-AePvjhyvsBo

Maximum Likelihood 1-1yJx-QtlvNI

Maximum Likelihood 2-6nUUeQ9AeUA

Quiz – Cross 1–xxrisIvD0E

Quiz Cross Entropy-njq6bYrPqSU

Cross Entropy 1-iREoPUrpXvE

CrossEntropy V1-1BnhC6e0TFw

Formula For Cross 1-qvr_ego_d6w

DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees

DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc

Error Function-V5kkHldUlVU

Gradient Descent-rhVIF-nigrY

Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA

Continuous Perceptrons-07-JJ-aGEfM

Non-Linear Data-F7ZiE8PQiSc

Non-Linear Models-HWuBKCZsCo8

29 Neural Network Architecture 2-FWN3Sw5fFoM

Combinando modelos-Boy3zHVrWB4

Layers-pg99FkXYK0M

Multiclass Classification-uNTtvxwfox0

DL 41 Feedforward FIX V2-hVCuvMGOfyY

DL 42 Neural Network Error Function (1)-SC1wEW7TtKs

Multilayer perceptrons-Rs9petvTBLk

Backpropagation V2-1SmY3TZTyUk