Section 1 : INTRODUCTION AND COURSE OUTLINE

 		Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 About Certification Pdf
Lecture 3 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 4 ML, AI and DL 12:0
Lecture 5 Machine Learning Big Picture 8:15
Lecture 6 About Proctor Testing Pdf
Lecture 7 Whats New in TensorFlow 2 15:7
Lecture 8 What is Google Colab 5:8
Lecture 9 Google Colab Demo 7:17
Lecture 10 Eager Execution 10:30
Lecture 11 Keras API 6:56
Lecture 12 About Certification Pdf

Section 2 : REVIEW OF ARTIFICIAL NEURAL NETWORKS AND CONVOLUTIONAL NEURAL NETWORKS

 		Lecture 13 ANN and CNN - Part 1 17:48
Lecture 14 ANN and CNN - Part 2 8:13
Lecture 15 ANN and CNN - Part 3 13:33
Lecture 16 ANN and CNN - Part 4 5:33
Lecture 17 ANN and CNN - Part 5 10:54
Lecture 18 ANN and CNN - Part 6 5:56
Lecture 19 ANN and CNN - Part 7 16:24
Lecture 20 ANN and CNN - Part 8
Lecture 21 Project 1 - Solution Part 1 6:6
Lecture 22 Project 1 - Solution Part 2 12:33

Section 3 : TRANSFER LEARNING (TF HUB)

 		Lecture 23 What is Transfer learning 8:26
Lecture 24 Transfer Learning Process 10:10
Lecture 25 Transfer Learning Strategies 7:54
Lecture 26 ImageNet 8:35
Lecture 27 Transfer Learning Project 1 - Coding P1 9:51
Lecture 28 Transfer Learning Project 1 - Coding P2 14:32
Lecture 29 Transfer Learning Project 1 - Coding P3 10:17
Lecture 30 Transfer Learning Project 1 - Coding P4 11:25
Lecture 31 Transfer Learning Project 1 - Coding P5 8:2
Lecture 32 Transfer Learning Project 2 - Coding P1 5:23
Lecture 33 Transfer Learning Project 2 - Coding P2 7:14
Lecture 34 Transfer Learning Project 2 - Coding P3 9:35

Section 4 : AUTOENCODERS

 		Lecture 35 Autoencoders intuition 12:29
Lecture 36 Autencoders Math 14:49
Lecture 37 Linear Autoencoders vs 5:53
Lecture 38 Autoencoders Applications 10:16
Lecture 39 Variational Autoencoders (VARS) 8:21
Lecture 40 Autoencoders CNN Dimensionality Review 9:37
Lecture 41 Autoencoders Project 1 - Coding P1 9:48
Lecture 42 Autoencoders Project 1 - Coding P2 8:44
Lecture 43 Autoencoders Project 1 - Coding P3 9:10
Lecture 44 Autoencoders Project 1 - Coding P4 9:32
Lecture 45 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 46 Autoencoders Project 2 - Coding P1 12:3
Lecture 47 Autoencoders Project 2 - Coding P2 21:14

Section 5 : DEEP DREAM

 		Lecture 48 What is Deep Dream 13:25
Lecture 49 How does DeepDream Algo work
Lecture 50 Deep Dream Simpified 6:36
Lecture 51 Deep Dream Coding P1
Lecture 52 Deep Dream Coding P2 9:4
Lecture 53 Deep Dream Coding P3 5:51
Lecture 54 Deep Dream Coding P4 11:39
Lecture 55 Deep Dream Coding P5 19:18

Section 6 : GANs

 		Lecture 56 GANS intuition 10:51
Lecture 57 Discriminator and Generator Networks 13:39
Lecture 58 Let's put the Discriminator and Generator together 13:39
Lecture 59 GAN Lab 12:19
Lecture 60 GANs applications
Lecture 61 GANS Project 1 P1 8:9
Lecture 62 GANS Project 1 P2 10:53
Lecture 63 GANS Project 1 P3 4:0
Lecture 64 GANS Project 1 P4 5:38
Lecture 65 GANS Project 1 P5 12:57

Section 7 : RECURRENT NEURAL NETWORKS (RNNs) AND LSTMs

 		Lecture 66 Recurrent Neural Networks Intuition 4:47
Lecture 67 RNN Architecture 9:17
Lecture 68 What makes RNN so special 6:51
Lecture 69 RNN Math 5:46
Lecture 70 Fun with RNN 7:7
Lecture 71 Vanishing Gradient Problem 12:19
Lecture 72 Long Short Term Memory LSTM
Lecture 73 RNN Project #1 - Part #1 8:18
Lecture 74 RNN Project #1 - Part #2 6:14
Lecture 75 RNN Project #1 - Part #3 5:44
Lecture 76 RNN Project #1 - Part #4 7:58

Section 8 : TENSORFLOW SERVING AND TENSORBOARD

 		Lecture 77 TF Serving Coding Part 1 9:11
Lecture 78 TF Serving Coding Part 2 7:50
Lecture 79 TF Serving Coding Part 3 12:18
Lecture 80 Tensorboard Example 1 12:23
Lecture 81 Tensorboard Example 2 9:21
Lecture 82 Distributed Strategy 3:10