Section 1 : Introduction

Lecture 1 Introduction copy 3:4
Lecture 2 Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks ) 6:58
Lecture 3 FAQ - Frequently Asked Questions Pdf

Section 2 : Installation and Setup

Lecture 4 Quick Note for MacOS and Linux Users Text
Lecture 5 Installing TensorFlow and Environment Setup 11:50

Section 3 : What is Machine Learning

Lecture 6 Machine Learning Overview 17:4

Section 4 : Crash Course Overview

Lecture 7 Crash Course Section Introduction 1:6
Lecture 8 NumPy Crash Course 15:21
Lecture 9 Pandas Crash Course 4:15
Lecture 10 Data Visualization Crash Course 7:32
Lecture 11 SciKit Learn Preprocessing Overview 8:55
Lecture 12 Crash Course Review Exercise 2:2
Lecture 13 Crash Course Review Exercise - Solutions 5:53

Section 5 : Introduction to Neural Networks

Lecture 14 Introduction to Neural Networks 1:1
Lecture 15 Introduction to Perceptron 5:6
Lecture 16 Neural Network Activation Functions 6:22
Lecture 17 Cost Functions 3:34
Lecture 18 Gradient Descent Backpropagation 3:15
Lecture 19 TensorFlow Playground 8:43
Lecture 20 Manual Creation of Neural Network - Part One 6:9
Lecture 21 Manual Creation of Neural Network - Part Two - Operations 7:43
Lecture 22 Manual Creation of Neural Network - Part Three - Placeholders and Variables 8:47
Lecture 23 Manual Creation of Neural Network - Part Four - Session 9:34
Lecture 24 Manual Neural Network Classification Task 16:20

Section 6 : TensorFlow Basics

Lecture 25 Introduction to TensorFlow 1:18
Lecture 26 TensorFlow Basic Syntax 12:35
Lecture 27 TensorFlow Graphs 5:42
Lecture 28 Variables and Placeholders 5:50
Lecture 29 TensorFlow - A Neural Network - Part One 7:37
Lecture 30 TensorFlow - A Neural Network - Part Two 19:42
Lecture 31 TensorFlow Regression Example - Part One 19:23
Lecture 32 TensorFlow Regression Example _ Part Two 21:38
Lecture 33 TensorFlow Classification Example - Part One 13:52
Lecture 34 TensorFlow Classification Example - Part Two 12:36
Lecture 35 TF Regression Exercise 3:11
Lecture 36 TF Regression Exercise Solution Walkthrough
Lecture 37 TF Classification Exercise 4:18
Lecture 38 TF Classification Exercise Solution Walkthrough 11:21
Lecture 39 Saving and Restoring Models 5:49

Section 7 : Convolutional Neural Networks

Lecture 40 Introduction to Convolutional Neural Network Section 0:44
Lecture 41 Review of Neural Networks 2:26
Lecture 42 New Theory Topics
Lecture 43 Quick note on MNIST lecture Text
Lecture 44 MNIST Data Overview 4:40
Lecture 45 MNIST Basic Approach Part One 8:24
Lecture 46 MNIST Basic Approach Part Two 16:39
Lecture 47 CNN Theory Part One 18:36
Lecture 48 CNN Theory Part Two 4:27
Lecture 49 CNN MNIST Code Along - Part One 17:18
Lecture 50 CNN MNIST Code Along - Part Two 5:58
Lecture 51 Introduction to CNN Project 5:56
Lecture 52 CNN Project Exercise Solution - Part One 15:15
Lecture 53 CNN Project Exercise Solution - Part Two 12:50

Section 8 : Recurrent Neural Networks

Lecture 54 Introduction to RNN Section 1:0
Lecture 55 RNN Theory 7:51
Lecture 56 Manual Creation of RNN 11:45
Lecture 57 Vanishing Gradients 4:31
Lecture 57 Vanishing Gradients 4:30
Lecture 58 LSTM and GRU Theory 9:41
Lecture 59 Introduction to RNN with TensorFlow API 4:33
Lecture 60 RNN with TensorFlow - Part One 20:42
Lecture 61 RNN with TensorFlow - Part Two
Lecture 62 Quick Note on RNN Plotting Part 3 Text
Lecture 63 RNN with TensorFlow - Part Three 7:44
Lecture 64 Time Series Exercise Overview 6:58
Lecture 65 Time Series Exercise Solution 18:8
Lecture 66 Quick Note on Word2Vec 2:43
Lecture 67 Word2Vec Theory 11:51
Lecture 68 Word2Vec Code Along - Part One 16:25
Lecture 69 Word2Vec Part Two 13:5

Section 9 : Miscellaneous Topics

Lecture 70 Intro to Miscellaneous Topics Text
Lecture 71 Deep Nets with Tensorflow Abstractions API - Part One 7:6
Lecture 72 Deep Nets with Tensorflow Abstractions API - Estimator API 7:18
Lecture 73 Deep Nets with Tensorflow Abstractions API - Keras 11:44
Lecture 74 Deep Nets with Tensorflow Abstractions API - Layers 10:35
Lecture 75 Tensorboard 15:59

Section 10 : AutoEncoders

Lecture 76 Autoencoder Basics 7:51
Lecture 77 Dimensionality Reduction with Linear Autoencoder 17:15
Lecture 78 Linear Autoencoder PCA Exercise Overview 1:38
Lecture 79 Linear Autoencoder PCA Exercise Solutions 7:44
Lecture 80 Stacked Autoencoder 19:25

Section 11 : Reinforcement Learning with OpenAI Gym

Lecture 81 Introduction to Reinforcement Learning with OpenAI Gym
Lecture 82 Extra Resources for Reinforcement Learning Pdf
Lecture 83 Introduction to OpenAI Gym 5:30
Lecture 84 OpenAI Gym Steup
Lecture 85 Open AI Gym Env Basics 5:35
Lecture 86 Open AI Gym Observations 7:59
Lecture 87 OpenAI Gym Actions 7:55
Lecture 88 Simple Neural Network Game 16:14
Lecture 89 Policy Gradient Theory 7:34
Lecture 90 Policy Gradient Code Along Part One 11:18
Lecture 91 Policy Gradient Code Along Part Two 12:16

Section 12 : GAN - Generative Adversarial Networks

Lecture 92 Introduction to GANs 7:6
Lecture 93 GAN Code Along - Part One 8:57
Lecture 94 GAN Code Along - Part Two 11:20
Lecture 95 GAN Code Along - Part Three 11:50