Section 1 : Introduction

Lecture 1 Why This Course 1:30
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Installation

Lecture 3 Overview 0:25
Lecture 4 Anaconda Distribution - Mac 2:42
Lecture 5 Anaconda Distribution - Windows 2:54
Lecture 6 Text Editor 2:47
Lecture 7 Outro 0:29

Section 3 : Python Crash Course (Optional)

Lecture 8 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 9 About Proctor Testing Pdf
Lecture 10 Python Crash Course Part 1 - Data Types 1:6
Lecture 11 Jupyter Notebooks 1:38
Lecture 12 Arithmetic Operations 4:23
Lecture 13 Variables 5:4
Lecture 14 Numeric Data Types 4:8
Lecture 15 String Data Types 5:45
Lecture 16 Booleans 4:27
Lecture 17 Methods 3:4
Lecture 18 Lists 5:29
Lecture 19 Slicing
Lecture 20 Membership Operators 2:49
Lecture 21 Mutability 4:8
Lecture 22 Mutability II 4:44
Lecture 23 Common Functions & Methods 7:30
Lecture 24 Tuples 3:31
Lecture 25 Sets 2:57
Lecture 26 Dictionaries 5:18
Lecture 27 Compound Data Structures 2:49
Lecture 28 Part 1 - Outro 0:14
Lecture 29 Part 2 - Control Flow 0:46
Lecture 30 If, else 4:46
Lecture 31 elif 6:51
Lecture 32 Complex Comparisons 5:10
Lecture 33 For Loops 7:16
Lecture 34 For Loops II 3:4
Lecture 35 While Loops 3:6
Lecture 36 Break 3:23
Lecture 37 Part 2 - Outro 0:16
Lecture 38 Part 3 - Functions 0:51
Lecture 39 Functions 5:34
Lecture 40 Scope
Lecture 41 Doc Strings 2:44
Lecture 42 Lambda & Higher Order Functions 6:6
Lecture 43 Part 3 - Outro 0:41

Section 4 : NumPy Crash Course (Optional)

Lecture 44 Overview 0:47
Lecture 45 Vector Addition - Arrays vs Lists 12:2
Lecture 46 Multidimensional Arrays 11:42
Lecture 47 One Dimensional Slicing 3:31
Lecture 48 Reshaping 3:34
Lecture 49 Multidimensional Slicing 7:19
Lecture 50 Manipulating Array Shapes 8:13
Lecture 51 Matrix Multiplication 4:18
Lecture 52 Stacking 13:57
Lecture 53 Part 4 - Outro 0:7

Section 5 : Computer Vision Finding Lane Lines

Lecture 54 Overview 0:35
Lecture 55 Image needed for the next lesson Text
Lecture 56 Loading Image 4:43
Lecture 57 About Proctor Testing Pdf
Lecture 58 Grayscale Conversion 4:29
Lecture 59 Smoothening Image 3:3
Lecture 60 Simple Edge Detection 4:18
Lecture 61 Region of Interest 7:39
Lecture 62 Binary Numbers & Bitwise_and 9:43
Lecture 63 Line Detection - Hough Transform 10:50
Lecture 64 Hough Transform II 13:19
Lecture 65 Optimizing
Lecture 66 Resource for upcoming video Text
Lecture 67 Finding Lanes on Video 6:16
Lecture 68 About Certification Pdf
Lecture 69 Source Code Text
Lecture 70 Part 5 - Conclusion 0:33

Section 6 : The Perceptron

Lecture 71 Overview 1:44
Lecture 72 Machine Learning 2:50
Lecture 73 Supervised Learning - Friendly Example 4:24
Lecture 74 Classification 7:47
Lecture 75 Linear Model
Lecture 76 Perceptrons 4:6
Lecture 77 Weights 2:2
Lecture 78 Project - Initial Stages 10:53
Lecture 79 Sample Code for Initial Stages Text
Lecture 80 Error Function 3:34
Lecture 81 Sigmoid 5:51
Lecture 82 Sigmoid Implementation (Code) 11:44
Lecture 83 Source code Text
Lecture 84 Cross Entropy 5:38
Lecture 85 Cross Entropy (Code) 7:40
Lecture 86 Source Code Text
Lecture 87 Gradient Descent 3:13
Lecture 88 Gradient Descent (Code) 8:44
Lecture 89 Recap 1:53
Lecture 90 Source Code Text
Lecture 91 Part 6 - Conclusion 0:39

Section 7 : Keras

Lecture 92 Overview 0:29
Lecture 93 Intro to Keras 2:4
Lecture 94 About Certification Pdf
Lecture 95 About Proctor Testing Pdf
Lecture 96 Starter Code Text
Lecture 97 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 98 Keras Models 21:8
Lecture 99 Keras - Predictions 19:20
Lecture 100 Source Code Text
Lecture 101 Part 7 - Outro 0:20

Section 8 : Deep Neural Networks

Lecture 102 Overview 0:51
Lecture 103 Non-Linear Boundaries 5:4
Lecture 104 Architecture 8:59
Lecture 105 Feedforward Process 7:44
Lecture 106 Error Function 4:9
Lecture 107 Backpropagation 5:9
Lecture 108 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 109 Code Implementation 25:59
Lecture 110 Source Code Text
Lecture 111 Section 8 - Conclusion 0:22

Section 9 : Multiclass Classification

Lecture 112 Overview 0:35
Lecture 113 Softmax 11:50
Lecture 114 Cross Entropy 8:14
Lecture 115 Implementation 30:54
Lecture 116 Source Code Text
Lecture 117 Section 9 - Outro 0:18

Section 10 : MNIST Image Recognition

Lecture 118 Overview 0:48
Lecture 119 MNIST Dataset 5:25
Lecture 120 Train & Test 13:27
Lecture 121 Hyperparameters 7:4
Lecture 122 Implementation Part 1 33:45
Lecture 123 About Certification Pdf
Lecture 124 Implementation Part 2 20:10
Lecture 125 Resource for upcoming video Text
Lecture 126 Implementation Part 3 11:48
Lecture 127 Final Source Code Text
Lecture 128 Section 10 - Outro 0:24

Section 11 : Convolutional Neural Networks

Lecture 129 Overview 0:45
Lecture 130 Convolutions & MNIST 6:44
Lecture 131 Convolutional Layer 18:11
Lecture 132 Convolutions II 8:6
Lecture 133 Pooling 14:10
Lecture 134 Fully Connected Layer 6:22
Lecture 135 Starter Code Text
Lecture 136 Code Implementation I 30:59
Lecture 137 Code Implementation II 26:19
Lecture 138 Final Source Code Text
Lecture 139 Section 11 - Conclusion 0:16

Section 12 : Classifying Road Symbols

Lecture 140 Overview 1:0
Lecture 141 Traffic Signs Starter Code Text
Lecture 142 Preprocessing Images 42:58
Lecture 143 leNet Implementation 20:11
Lecture 144 Fine-tuning Model 14:27
Lecture 145 Resources Needed for Testing Text
Lecture 146 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 147 Fit Generator 23:50
Lecture 148 Final Source Code Text
Lecture 149 Section 12 - Outro 0:42

Section 13 : Polynomial Regression

Lecture 150 Overview 0:29
Lecture 151 Implementation 15:22
Lecture 152 Final Source Code Text
Lecture 153 Section 13 - Conclusion 0:22

Section 14 : Behavioural Cloning

Lecture 154 Overview 3:11
Lecture 155 Collecting Data 17:45
Lecture 156 Downloading Data 17:52
Lecture 157 Balancing Data 11:31
Lecture 158 Training & Validation Split 11:26
Lecture 159 Preprocessing Images
Lecture 160 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 161 Defining Nvidia Model 27:9
Lecture 162 Drive Text
Lecture 163 About Certification Pdf
Lecture 164 Flask & Socket 17:33
Lecture 165 Self Driving Car - Test 1 16:30
Lecture 166 About Proctor Testing Pdf
Lecture 167 Generator - Augmentation Techniques 34:28
Lecture 168 Batch Generator 10:58
Lecture 169 Fit Generator 19:17
Lecture 170 Final Source Code Text
Lecture 171 Outro 0:54