Section 1 : Deep Learning A-Z™ Hands-On Artificial Neural Networks

 		lecture 1 What is Deep Learning 12:34
lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 3 BONUS Learning Paths Text
lecture 4 BONUS Meet Your Instructors Text
lecture 5 Some Additional Resources!! Text
lecture 6 FAQBot! Text
lecture 7 Get the materials Text
lecture 8 Your Shortcut To Becoming A Better Data Scienti Text

Section 2 : Part 1 - Artificial Neural Networks

lecture 9 Welcome to Part 1 - Artificial Neural Networks Text

Section 3 : ANN Intuition

 		lecture 10 What You'll Need for ANN Text
lecture 11 Plan of Attack 2:52
lecture 12 The Neuron 16:15
lecture 13 The Activation Function 8:29
lecture 14 How do Neural Networks work 12:48
lecture 15 How do Neural Networks learn 12:59
lecture 16 Gradient Descent 10:13
lecture 17 Stochastic Gradient Descent 8:45
lecture 18 Backpropagation 5:22

Section 4 : Building an ANN

 		lecture 19 Business Problem Description 4:59
lecture 20 IMPORTANT NOTE Text
lecture 21 Building an ANN - Step 1 10:21
lecture 22 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 23 Building an ANN - Step 2 18:37
lecture 24 Building an ANN - Step 3 14:28
lecture 25 Building an ANN - Step 4
lecture 26 Building an ANN - Step 5 16:25

Section 5 : Part 2 - Convolutional Neural Networks

lecture 27 Welcome to Part 2 - Convolutional Neural Netwo Text

Section 6 : CNN Intuition

 		lecture 28 What You'll Need for CNN Text
lecture 29 Plan of attack 3:32
lecture 30 What are convolutional neural networks 15:49
lecture 31 Step 1 - Convolution Operation
lecture 32 Step 1(b) - ReLU Layer 6:41
lecture 33 Step 2 - Pooling 14:13
lecture 34 Step 3 - Flattening 1:53
lecture 35 Step 4 - Full Connection 19:25
lecture 36 Summary 4:20
lecture 37 Softmax & Cross-Entropy 18:20

Section 7 : Building a CNN

 		lecture 38 IMPORTANT NOTE Text
lecture 39 Building a CNN - Step 1 11:35
lecture 40 Building a CNN - Step 2 17:46
lecture 41 Building a CNN - Step 3
lecture 42 Building a CNN - Step 4 7:21
lecture 43 Building a CNN - Step 5 14:56
lecture 44 About Certification Pdf
lecture 45 Building a CNN - FINAL DEMO! 23:38

Section 8 : Part 3 - Recurrent Neural Networks

lecture 46 Welcome to Part 3 - Recurrent Neural Networks Text

Section 9 : RNN Intuition

 		lecture 47 About Proctor Testing Pdf
lecture 48 Plan of attack 2:32
lecture 49 The idea behind Recurrent Neural Networks 16:2
lecture 50 The Vanishing Gradient Problem 14:27
lecture 51 LSTMs 19:48
lecture 52 Practical intuition 15:11
lecture 53 EXTRA LSTM Variations 3:37

Section 10 : Building a RNN

 		lecture 54 IMPORTANT NOTE Text
lecture 55 Building a RNN - Step 1 6:29
lecture 56 Building a RNN - Step 2 7:4
lecture 57 Building a RNN - Step 3 5:58
lecture 58 Building a RNN - Step 4 14:23
lecture 59 Building a RNN - Step 5 10:40
lecture 60 Building a RNN - Step 6 2:50
lecture 61 Building a RNN - Step 7 8:43
lecture 62 Building a RNN - Step 8 5:20
lecture 63 Building a RNN - Step 9 3:20
lecture 64 Building a RNN - Step 10 4:22
lecture 65 Building a RNN - Step 11 10:31
lecture 66 Building a RNN - Step 12 5:23
lecture 67 Building a RNN - Step 13 16:51
lecture 68 Building a RNN - Step 14 8:15
lecture 69 Building a RNN - Step 1 9:36

Section 11 : Evaluating and Improving the RNN

 		lecture 70 Evaluating the RNN Text
lecture 71 Improving the RNN Text

Section 12 : Part 4 - Self Organizing Maps

lecture 72 Welcome to Part 4 - Self Organizing Maps Text

Section 13 : SOMs Intuition

 		lecture 73 Plan of attack 3:10
lecture 74 How do Self-Organizing Maps Work 8:30
lecture 75 Why revisit K-Means 2:20
lecture 76 K-Means Clustering (Refresher)
lecture 77 How do Self-Organizing Maps Learn (Part 1) 14:24
lecture 78 How do Self-Organizing Maps Learn (Part 2) 9:37
lecture 79 Live SOM example 2:49
lecture 80 Reading an Advanced SOM 14:26
lecture 81 EXTRA K-means Clustering (part 2) 7:49
lecture 82 EXTRA K-means Clustering (part 3) 11:52

Section 14 : Building a SOM

 		lecture 83 IMPORTANT NOTE Text
lecture 84 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 85 Building a SOM - Step 1 13:42
lecture 86 Building a SOM - Step 2 9:40
lecture 87 Building a SOM - Step 3 17:25
lecture 88 Building a SOM - Step 4 11:12
lecture 89 IMPORTANT NOTE Text

Section 15 : Mega Case Study

 		lecture 90 Mega Case Study - Step 1 2:49
lecture 91 Mega Case Study - Step 2 4:17
lecture 92 Mega Case Study - Step 3 14:37
lecture 93 Mega Case Study - Step 4 9:2

Section 16 : Part 5 - Boltzmann Machines

lecture 94 Welcome to Part 5 - Boltzmann Machines Text
lecture 95 Plan of attack 2:24

Section 17 : Boltzmann Machine Intuition

 		lecture 96 Boltzmann Machine 14:22
lecture 97 Energy-Based Models (EBM) 10:39
lecture 98 Editing Wikipedia - Our Contribution to the Wo 3:28
lecture 99 Restricted Boltzmann Machine 17:29
lecture 100 Contrastive Divergence 16:29
lecture 101 Deep Belief Networks 5:24
lecture 102 Deep Boltzmann Machines 2:57
lecture 103 About Certification Pdf

Section 18 : Building a Boltzmann Machine

 		lecture 104 IMPORTANT NOTE Text
lecture 105 Installing PyTorch Text
lecture 106 Building a Boltzmann Machine - Introduction 9:10
lecture 107 Same Data Preprocessing in Parts 5 and 6 Text
lecture 108 Building a Boltzmann Machine - Step 1 9:13
lecture 109 Building a Boltzmann Machine - Step 2 9:40
lecture 110 Building a Boltzmann Machine - Step 3 8:21
lecture 111 Building a Boltzmann Machine - Step 4 20:53
lecture 112 Building a Boltzmann Machine - Step 5 5:5
lecture 113 Building a Boltzmann Machine - Step 6 7:34
lecture 114 Building a Boltzmann Machine - Step 7 10:14
lecture 115 Building a Boltzmann Machine - Step 8 12:36
lecture 116 Building a Boltzmann Machine - Step 9 6:17
lecture 117 Building a Boltzmann Machine - Step 10 11:35
lecture 118 Building a Boltzmann Machine - Step 11 6:58
lecture 119 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 120 Building a Boltzmann Machine - Step 13 18:42
lecture 121 Building a Boltzmann Machine - Step 14 17:10
lecture 122 Evaluating the Boltzmann Machine Text

Section 19 : Part 6 - AutoEncoders

lecture 123 Welcome to Part 6 - AutoEncoders Text
lecture 124 Plan of attack 2:12

Section 20 : AutoEncoders Intuition

 		lecture 125 Auto Encoders 10:50
lecture 126 A Note on Biases 1:16
lecture 127 Training an Auto Encoder 6:10
lecture 128 Overcomplete hidden layers 3:53
lecture 129 Sparse Autoencoders 6:15
lecture 130 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 131 Contractive Autoencoders 2:23
lecture 132 Stacked Autoencoders 1:54
lecture 133 Deep Autoencoders 1:51

Section 21 : Building an AutoEncoder

 		lecture 134 IMPORTANT NOTE Text
lecture 135 About Certification Pdf
lecture 136 Installing PyTorch Text
lecture 137 Same Data Preprocessing in Parts 5 and 6 Text
lecture 138 Building an AutoEncoder - Step 1 12:5
lecture 139 Building an AutoEncoder - Step 2 11:50
lecture 140 Building an AutoEncoder - Step 3 8:21
lecture 141 Homework Challenge - Coding Exercise Text
lecture 142 Building an AutoEncoder - Step 4 20:51
lecture 143 Building an AutoEncoder - Step 5 5:5
lecture 144 Building an AutoEncoder - Step 6 16:46
lecture 145 Building an AutoEncoder - Step 7 13:37
lecture 146 Building an AutoEncoder - Step 8
lecture 147 Building an AutoEncoder - Step 9 13:33
lecture 148 Building an AutoEncoder - Step 10 4:22
lecture 149 Building an AutoEncoder - Step 11 11:26
lecture 150 About Proctor Testing Pdf

Section 22 : Annex - Get the Machine Learning Basics

lecture 151 Annex - Get the Machine Learning Basics Text

Section 23 : Regression & Classification Intuition

 		lecture 152 What You Need for Regression & Classification Text
lecture 153 Simple Linear Regression Intuition - Step 1 5:46
lecture 154 Simple Linear Regression Intuition - Step 2 3:9
lecture 155 Multiple Linear Regression Intuition 1:3
lecture 156 Logistic Regression Intuition 17:7

Section 24 : Data Preprocessing Template

 		lecture 157 Important Instructions Text
lecture 158 About Certification Pdf
lecture 159 Data Preprocessing - Step 2 3:34
lecture 160 Data Preprocessing - Step 3 15:42
lecture 161 Data Preprocessing - Step 4 12:15
lecture 162 Data Preprocessing - Step 5 14:58
lecture 163 Data Preprocessing - Step 6 13:47
lecture 164 Data Preprocessing - Step 7 20:31

Section 25 : Logistic Regression Implementation

 		lecture 165 Important Instructions Text
lecture 166 Logistic Regression - Step 1 9:43
lecture 167 Logistic Regression - Step 2 13:38
lecture 168 Logistic Regression - Step 3 7:40
lecture 169 Logistic Regression - Step 4 7:49
lecture 170 Logistic Regression - Step 5 6:15
lecture 171 Logistic Regression - Step 6 9:26
lecture 172 Logistic Regression - Step 7 16:6