Section 1 : Introduction
Lecture 1 1. Lecture 1 - INTRODUCTION TO BRAINMEASURES PROCT
Lecture 2 Introduction + Course Structure + Demo 00:16:44 Duration
Lecture 3 BONUS Learning Paths
Lecture 4 Your Three Best Resources 00:10:43 Duration
Lecture 5 Download the Resources here
Lecture 6 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 2 : Step 1 - Artificial Neural Network
Lecture 1 Welcome to Step 1 - Artificial Neural Network
Lecture 2 Plan of Attack 00:02:52 Duration
Lecture 3 The Neuron 00:16:15 Duration
Lecture 4 The Activation Function
Lecture 5 How do Neural Networks work 00:12:48 Duration
Lecture 6 How do Neural Networks learn 00:12:59 Duration
Lecture 7 Gradient Descent 00:10:13 Duration
Lecture 8 Stochastic Gradient Descent
Lecture 9 Backpropagation 00:05:22 Duration

Section 3 : Step 2 - Convolutional Neural Network
Lecture 1 Welcome to Step 2 - Convolutional Neural Network
Lecture 2 Plan of Attack 00:03:32 Duration
Lecture 3 What are Convolutional Neural Networks 00:15:49 Duration
Lecture 4 Step 1 - The Convolution Operation
Lecture 5 Step 1 Bis - The ReLU Layer 00:06:41 Duration
Lecture 6 Step 2 - Pooling 00:14:13 Duration
Lecture 7 Step 3 - Flattening 00:01:53 Duration
Lecture 8 Step 4 - Full Connection 00:19:25 Duration
Lecture 9 Summary 00:04:20 Duration
Lecture 10 Softmax & Cross-Entropy 00:18:20 Duration

Section 4 : Step 3 - AutoEncoder
Lecture 1 Welcome to Step 3 - AutoEncoder
Lecture 2 Plan of Attack 00:02:12 Duration
Lecture 3 What are AutoEncoders 00:10:50 Duration
Lecture 4 A Note on Biases
Lecture 5 Training an AutoEncoder 00:06:10 Duration
Lecture 6 Overcomplete Hidden Layers 00:03:53 Duration
Lecture 7 Sparse AutoEncoders 00:06:15 Duration

Section 5 : Step 4 - Variational AutoEncoder
Lecture 1 Welcome to Step 4 - Variational AutoEncoder
Lecture 2 Introduction to the VAE 00:08:16 Duration
Lecture 3 Variational AutoEncoders. 00:04:29 Duration
Lecture 4 Reparameterization Trick 00:04:56 Duration

Section 6 : Implementing the CNN-VAE
Lecture 1 Welcome to Step 5 - Implementing the CNN-VAE
Lecture 2 Introduction to Step 5 00:08:11 Duration
Lecture 3 Initializing all the parameters and variables of 00:13:54 Duration
Lecture 4 Building the Encoder part of the VAE 00:19:34 Duration
Lecture 5 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SY
Lecture 6 Building the Decoder part of the VAE 00:10:40 Duration
Lecture 7 Implementing the Training operations 00:18:34 Duration
Lecture 8 Full Code Section
Lecture 9 The Keras Implementation

Section 7 : Step 6 - Recurrent Neural Network
Lecture 1 Welcome to Step 6 - Recurrent Neural Network
Lecture 2 Plan of Attack. 00:02:32 Duration
Lecture 3 What are Recurrent Neural Networks 00:16:02 Duration
Lecture 4 The Vanishing Gradient Problem 00:14:27 Duration
Lecture 5 LSTMs 00:19:48 Duration
Lecture 6 LSTM Practical Intuition
Lecture 7 LSTM Variations 00:03:37 Duration

Section 8 : Step 7 - Mixture Density Network
Lecture 1 Welcome to Step 7 - Mixture Density Network
Lecture 2 Introduction to the MDN-RNN 00:09:28 Duration
Lecture 3 Mixture Density Networks 00:09:33 Duration
Lecture 4 VAE + MDN-RNN Visualization 00:05:46 Duration

Section 9 : Step 8 - Implementing the MDN-RNN
Lecture 1 . Welcome to Step 8 - Implementing the MDN-RNN
Lecture 2 Initializing all the parameters and variables 00:13:42 Duration
Lecture 3 Building the RNN - Gathering the parameters 00:09:54 Duration
Lecture 4 Building the RNN - Creating an LSTM cell with Drop 00:16:15 Duration
Lecture 5 Building the RNN - Setting up the Input, Target, 00:14:54 Duration
Lecture 6 Building the RNN - Getting the Deterministic Outpu 00:11:56 Duration
Lecture 7 Building the MDN - Getting the Input, Hidden Layer 00:13:22 Duration
Lecture 8 Building the MDN - Getting the MDN parameters. 00:10:57 Duration
Lecture 9 mplementing the Training operations (Part 1 00:15:31 Duration
Lecture 10 Implementing the Training operations (Part 2 00:13:34 Duration
Lecture 11 Full Code Section
Lecture 12 The Keras Implementation

Section 10 : Step 9 - Reinforcement Learning
Lecture 1 Welcome to Step 9 - Reinforcement Learning
Lecture 2 What is Reinforcement Learning. 00:11:27 Duration
Lecture 3 Pseudo Implementation of Reinforcement Learning 00:20:00 Duration
Lecture 4 Full Code Section

Section 11 : Step 10 - Deep NeuroEvolution
Lecture 1 Welcome to Step 10 - Deep NeuroEvolution
Lecture 2 Deep NeuroEvolution 00:11:10 Duration
Lecture 3 Evolution Strategies 00:09:27 Duration
Lecture 4 Genetic Algorithms 00:12:31 Duration
Lecture 5 Covariance-Matrix Adaptation Evolution Strategy 00:13:26 Duration
Lecture 6 Parameter-Exploring Policy Gradients (PEPG). 00:12:55 Duration
Lecture 7 OpenAI OpenAI EvEvolution Strategolution Strategy. 00:08:30 Duration

Section 12 : The Final Run
Lecture 1 The Whole Implementatio 00:19:50 Duration
Lecture 2 Download the whole AI Masterclass folder here
Lecture 3 Installing the required packages 00:11:38 Duration
Lecture 4 The Final Race Human Intelligence vs. Artificial 00:10:16 Duration