Section 1 : Deep Learning A-Z™ Hands-On Artificial Neural Networks
Lecture 1 What is Deep Learning 00:12:34 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 3 BONUS Learning Paths
Lecture 4 BONUS Meet Your Instructors
Lecture 5 Some Additional Resources!!
Lecture 6 FAQBot!
Lecture 7 Get the materials
Lecture 8 Your Shortcut To Becoming A Better Data Scienti

Section 2 : Part 1 - Artificial Neural Networks

Lecture 1 Welcome to Part 1 - Artificial Neural Networks

Section 3 : ANN Intuition
Lecture 1 What You'll Need for ANN
Lecture 2 Plan of Attack 00:02:52 Duration
Lecture 3 The Neuron 00:16:15 Duration
Lecture 4 The Activation Function 00:08:29 Duration
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 00:08:45 Duration
Lecture 9 Backpropagation 00:05:22 Duration

Section 4 : Building an ANN
Lecture 1 Business Problem Description 00:04:59 Duration
Lecture 2 IMPORTANT NOTE
Lecture 3 Building an ANN - Step 1 00:10:21 Duration
Lecture 4 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 5 Building an ANN - Step 2 00:18:37 Duration
Lecture 6 Building an ANN - Step 3 00:14:28 Duration
Lecture 7 Building an ANN - Step 4
Lecture 8 Building an ANN - Step 5 00:16:25 Duration

Section 5 : Part 2 - Convolutional Neural Networks

Lecture 1 Welcome to Part 2 - Convolutional Neural Netwo

Section 6 : CNN Intuition
Lecture 1 What You'll Need for CNN
Lecture 2 Plan of attack 00:03:32 Duration
Lecture 3 What are convolutional neural networks 00:15:49 Duration
Lecture 4 Step 1 - Convolution Operation
Lecture 5 Step 1(b) - 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 7 : Building a CNN
Lecture 1 IMPORTANT NOTE
Lecture 2 Building a CNN - Step 1 00:11:35 Duration
Lecture 3 Building a CNN - Step 2 00:17:46 Duration
Lecture 4 Building a CNN - Step 3
Lecture 5 Building a CNN - Step 4 00:07:21 Duration
Lecture 6 Building a CNN - Step 5 00:14:56 Duration
Lecture 7 About Certification
Lecture 8 Building a CNN - FINAL DEMO! 00:23:38 Duration

Section 8 : Part 3 - Recurrent Neural Networks

Lecture 1 Welcome to Part 3 - Recurrent Neural Networks

Section 9 : RNN Intuition
Lecture 1 About Proctor Testing
Lecture 2 Plan of attack 00:02:32 Duration
Lecture 3 The idea behind 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 Practical intuition 00:15:11 Duration
Lecture 7 EXTRA LSTM Variations 00:03:37 Duration

Section 10 : Building a RNN
Lecture 1 IMPORTANT NOTE
Lecture 2 Building a RNN - Step 1 00:06:29 Duration
Lecture 3 Building a RNN - Step 2 00:07:04 Duration
Lecture 4 Building a RNN - Step 3 00:05:58 Duration
Lecture 5 Building a RNN - Step 4 00:14:23 Duration
Lecture 6 Building a RNN - Step 5 00:10:40 Duration
Lecture 7 Building a RNN - Step 6 00:02:50 Duration
Lecture 8 Building a RNN - Step 7 00:08:43 Duration
Lecture 9 Building a RNN - Step 8 00:05:20 Duration
Lecture 10 Building a RNN - Step 9 00:03:20 Duration
Lecture 11 Building a RNN - Step 10 00:04:22 Duration
Lecture 12 Building a RNN - Step 11 00:10:31 Duration
Lecture 13 Building a RNN - Step 12 00:05:23 Duration
Lecture 14 Building a RNN - Step 13 00:16:51 Duration
Lecture 15 Building a RNN - Step 14 00:08:15 Duration
Lecture 16 Building a RNN - Step 1 00:09:36 Duration

Section 11 : Evaluating and Improving the RNN
Lecture 1 Evaluating the RNN
Lecture 2 Improving the RNN

Section 12 : Part 4 - Self Organizing Maps

Lecture 1 Welcome to Part 4 - Self Organizing Maps

Section 13 : SOMs Intuition
Lecture 1 Plan of attack 00:03:10 Duration
Lecture 2 How do Self-Organizing Maps Work 00:08:30 Duration
Lecture 3 Why revisit K-Means 00:02:20 Duration
Lecture 4 K-Means Clustering (Refresher)
Lecture 5 How do Self-Organizing Maps Learn (Part 1) 00:14:24 Duration
Lecture 6 How do Self-Organizing Maps Learn (Part 2) 00:09:37 Duration
Lecture 7 Live SOM example 00:02:49 Duration
Lecture 8 Reading an Advanced SOM 00:14:26 Duration
Lecture 9 EXTRA K-means Clustering (part 2) 00:07:49 Duration
Lecture 10 EXTRA K-means Clustering (part 3) 00:11:52 Duration

Section 14 : Building a SOM
Lecture 1 IMPORTANT NOTE
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 3 Building a SOM - Step 1 00:13:42 Duration
Lecture 4 Building a SOM - Step 2 00:09:40 Duration
Lecture 5 Building a SOM - Step 3 00:17:25 Duration
Lecture 6 Building a SOM - Step 4 00:11:12 Duration
Lecture 7 IMPORTANT NOTE

Section 15 : Mega Case Study
Lecture 1 Mega Case Study - Step 1 00:02:49 Duration
Lecture 2 Mega Case Study - Step 2 00:04:17 Duration
Lecture 3 Mega Case Study - Step 3 00:14:37 Duration
Lecture 4 Mega Case Study - Step 4 00:09:02 Duration

Section 16 : Part 5 - Boltzmann Machines

Lecture 1 Welcome to Part 5 - Boltzmann Machines
Lecture 2 Plan of attack 00:02:24 Duration

Section 17 : Boltzmann Machine Intuition
Lecture 1 Boltzmann Machine 00:14:22 Duration
Lecture 2 Energy-Based Models (EBM) 00:10:39 Duration
Lecture 3 Editing Wikipedia - Our Contribution to the Wo 00:03:28 Duration
Lecture 4 Restricted Boltzmann Machine 00:17:29 Duration
Lecture 5 Contrastive Divergence 00:16:29 Duration
Lecture 6 Deep Belief Networks 00:05:24 Duration
Lecture 7 Deep Boltzmann Machines 00:02:57 Duration
Lecture 8 About Certification

Section 18 : Building a Boltzmann Machine
Lecture 1 IMPORTANT NOTE
Lecture 2 Installing PyTorch
Lecture 3 Building a Boltzmann Machine - Introduction 00:09:10 Duration
Lecture 4 Same Data Preprocessing in Parts 5 and 6
Lecture 5 Building a Boltzmann Machine - Step 1 00:09:13 Duration
Lecture 6 Building a Boltzmann Machine - Step 2 00:09:40 Duration
Lecture 7 Building a Boltzmann Machine - Step 3 00:08:21 Duration
Lecture 8 Building a Boltzmann Machine - Step 4 00:20:53 Duration
Lecture 9 Building a Boltzmann Machine - Step 5 00:05:05 Duration
Lecture 10 Building a Boltzmann Machine - Step 6 00:07:34 Duration
Lecture 11 Building a Boltzmann Machine - Step 7 00:10:14 Duration
Lecture 12 Building a Boltzmann Machine - Step 8 00:12:36 Duration
Lecture 13 Building a Boltzmann Machine - Step 9 00:06:17 Duration
Lecture 14 Building a Boltzmann Machine - Step 10 00:11:35 Duration
Lecture 15 Building a Boltzmann Machine - Step 11 00:06:58 Duration
Lecture 16 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 17 Building a Boltzmann Machine - Step 13 00:18:42 Duration
Lecture 18 Building a Boltzmann Machine - Step 14 00:17:10 Duration
Lecture 19 Evaluating the Boltzmann Machine

Section 19 : Part 6 - AutoEncoders

Lecture 1 Welcome to Part 6 - AutoEncoders
Lecture 2 Plan of attack 00:02:12 Duration

Section 20 : AutoEncoders Intuition
Lecture 1 Auto Encoders 00:10:50 Duration
Lecture 2 A Note on Biases 00:01:16 Duration
Lecture 3 Training an Auto Encoder 00:06:10 Duration
Lecture 4 Overcomplete hidden layers 00:03:53 Duration
Lecture 5 Sparse Autoencoders 00:06:15 Duration
Lecture 6 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 7 Contractive Autoencoders 00:02:23 Duration
Lecture 8 Stacked Autoencoders 00:01:54 Duration
Lecture 9 Deep Autoencoders 00:01:51 Duration

Section 21 : Building an AutoEncoder
Lecture 1 IMPORTANT NOTE
Lecture 2 About Certification
Lecture 3 Installing PyTorch
Lecture 4 Same Data Preprocessing in Parts 5 and 6
Lecture 5 Building an AutoEncoder - Step 1 00:12:05 Duration
Lecture 6 Building an AutoEncoder - Step 2 00:11:50 Duration
Lecture 7 Building an AutoEncoder - Step 3 00:08:21 Duration
Lecture 8 Homework Challenge - Coding Exercise
Lecture 9 Building an AutoEncoder - Step 4 00:20:51 Duration
Lecture 10 Building an AutoEncoder - Step 5 00:05:05 Duration
Lecture 11 Building an AutoEncoder - Step 6 00:16:46 Duration
Lecture 12 Building an AutoEncoder - Step 7 00:13:37 Duration
Lecture 13 Building an AutoEncoder - Step 8
Lecture 14 Building an AutoEncoder - Step 9 00:13:33 Duration
Lecture 15 Building an AutoEncoder - Step 10 00:04:22 Duration
Lecture 16 Building an AutoEncoder - Step 11 00:11:26 Duration
Lecture 17 About Proctor Testing

Section 22 : Annex - Get the Machine Learning Basics

Lecture 1 Annex - Get the Machine Learning Basics

Section 23 : Regression & Classification Intuition
Lecture 1 What You Need for Regression & Classification
Lecture 2 Simple Linear Regression Intuition - Step 1 00:05:46 Duration
Lecture 3 Simple Linear Regression Intuition - Step 2 00:03:09 Duration
Lecture 4 Multiple Linear Regression Intuition 00:01:03 Duration
Lecture 5 Logistic Regression Intuition 00:17:07 Duration

Section 24 : Data Preprocessing Template
Lecture 1 Important Instructions
Lecture 2 About Certification
Lecture 3 Data Preprocessing - Step 2 00:03:34 Duration
Lecture 4 Data Preprocessing - Step 3 00:15:42 Duration
Lecture 5 Data Preprocessing - Step 4 00:12:15 Duration
Lecture 6 Data Preprocessing - Step 5 00:14:58 Duration
Lecture 7 Data Preprocessing - Step 6 00:13:47 Duration
Lecture 8 Data Preprocessing - Step 7 00:20:31 Duration

Section 25 : Logistic Regression Implementation
Lecture 1 Important Instructions
Lecture 2 Logistic Regression - Step 1 00:09:43 Duration
Lecture 3 Logistic Regression - Step 2 00:13:38 Duration
Lecture 4 Logistic Regression - Step 3 00:07:40 Duration
Lecture 5 Logistic Regression - Step 4 00:07:49 Duration
Lecture 6 Logistic Regression - Step 5 00:06:15 Duration
Lecture 7 Logistic Regression - Step 6 00:09:26 Duration
Lecture 8 Logistic Regression - Step 7 00:16:06 Duration