Section 1 : Introduction

 		Lecture 1 1. Lecture 1 - INTRODUCTION TO BRAINMEASURES PROCT Pdf
lecture 2 Introduction + Course Structure + Demo 16:44
lecture 3 BONUS Learning Paths Text
lecture 4 Your Three Best Resources 10:43
lecture 5 Download the Resources here Text
lecture 6 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Step 1 - Artificial Neural Network

 		lecture 7 Welcome to Step 1 - Artificial Neural Network Text
Lecture 8 Plan of Attack 2:52
Lecture 9 The Neuron 16:15
Lecture 10 The Activation Function
Lecture 11 How do Neural Networks work 12:48
Lecture 12 How do Neural Networks learn 12:59
Lecture 13 Gradient Descent 10:13
Lecture 14 Stochastic Gradient Descent
Lecture 15 Backpropagation 5:22

Section 3 : Step 2 - Convolutional Neural Network

 		Lecture 16 Welcome to Step 2 - Convolutional Neural Network Text
Lecture 17 Plan of Attack 3:32
Lecture 18 What are Convolutional Neural Networks 15:49
Lecture 19 Step 1 - The Convolution Operation
Lecture 20 Step 1 Bis - The ReLU Layer 6:41
Lecture 21 Step 2 - Pooling 14:13
Lecture 22 Step 3 - Flattening 1:53
Lecture 23 Step 4 - Full Connection 19:25
Lecture 24 Summary 4:20
Lecture 25 Softmax & Cross-Entropy 18:20

Section 4 : Step 3 - AutoEncoder

 		Lecture 26 Welcome to Step 3 - AutoEncoder Text
Lecture 27 Plan of Attack 2:12
Lecture 28 What are AutoEncoders 10:50
Lecture 29 A Note on Biases
Lecture 30 Training an AutoEncoder 6:10
Lecture 31 Overcomplete Hidden Layers 3:53
Lecture 32 Sparse AutoEncoders 6:15

Section 5 : Step 4 - Variational AutoEncoder

 		Lecture 37 Welcome to Step 4 - Variational AutoEncoder Text
Lecture 38 Introduction to the VAE 8:16
Lecture 39 Variational AutoEncoders. 4:29
Lecture 40 Reparameterization Trick 4:56

Section 6 : Implementing the CNN-VAE

 		Lecture 41 Welcome to Step 5 - Implementing the CNN-VAE Text
Lecture 42 Introduction to Step 5 8:11
Lecture 43 Initializing all the parameters and variables of 13:54
Lecture 44 Building the Encoder part of the VAE 19:34
Lecture 45 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SY Pdf
Lecture 46 Building the Decoder part of the VAE 10:40
Lecture 47 Implementing the Training operations 18:34
Lecture 48 Full Code Section Text
Lecture 49 The Keras Implementation Text

Section 7 : Step 6 - Recurrent Neural Network

 		Lecture 50 Welcome to Step 6 - Recurrent Neural Network Text
Lecture 51 Plan of Attack. 2:32
Lecture 52 What are Recurrent Neural Networks 16:2
Lecture 53 The Vanishing Gradient Problem 14:27
Lecture 54 LSTMs 19:48
Lecture 55 LSTM Practical Intuition
Lecture 56 LSTM Variations 3:37

Section 8 : Step 7 - Mixture Density Network

 		Lecture 57 Welcome to Step 7 - Mixture Density Network Text
Lecture 58 Introduction to the MDN-RNN 9:28
Lecture 59 Mixture Density Networks 9:33
Lecture 60 VAE + MDN-RNN Visualization 5:46

Section 9 : Step 8 - Implementing the MDN-RNN

 		Lecture 61 . Welcome to Step 8 - Implementing the MDN-RNN Text
Lecture 62 Initializing all the parameters and variables 13:42
Lecture 63 Building the RNN - Gathering the parameters 9:54
Lecture 64 Building the RNN - Creating an LSTM cell with Drop 16:15
Lecture 65 Building the RNN - Setting up the Input, Target, 14:54
Lecture 66 Building the RNN - Getting the Deterministic Outpu 11:56
Lecture 67 Building the MDN - Getting the Input, Hidden Layer 13:22
Lecture 68 Building the MDN - Getting the MDN parameters. 10:57
Lecture 69 mplementing the Training operations (Part 1 15:31
Lecture 70 Implementing the Training operations (Part 2 13:34
Lecture 71 Full Code Section Text
Lecture 72 The Keras Implementation Text

Section 10 : Step 9 - Reinforcement Learning

 		Lecture 73 Welcome to Step 9 - Reinforcement Learning Text
Lecture 74 What is Reinforcement Learning. 11:27
Lecture 75 Pseudo Implementation of Reinforcement Learning 20:0
Lecture 76 Full Code Section Text

Section 11 : Step 10 - Deep NeuroEvolution

 		Lecture 77 Welcome to Step 10 - Deep NeuroEvolution Text
Lecture 78 Deep NeuroEvolution 11:10
Lecture 79 Evolution Strategies 9:27
Lecture 80 Genetic Algorithms 12:31
Lecture 81 Covariance-Matrix Adaptation Evolution Strategy 13:26
Lecture 82 Parameter-Exploring Policy Gradients (PEPG). 12:55
Lecture 83 OpenAI OpenAI EvEvolution Strategolution Strategy. 8:30

Section 12 : The Final Run

 		Lecture 84 The Whole Implementatio 19:50
Lecture 85 Download the whole AI Masterclass folder here Text
Lecture 86 Installing the required packages 11:38
Lecture 87 The Final Race Human Intelligence vs. Artificial 10:16