#### 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