Section 1 : Introduction
Lecture 1 Introduction copy 00:03:04 Duration
Lecture 2 Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks ) 00:06:58 Duration
Lecture 3 FAQ - Frequently Asked Questions

Section 2 : Installation and Setup
Lecture 1 Quick Note for MacOS and Linux Users
Lecture 2 Installing TensorFlow and Environment Setup 00:11:50 Duration

Section 3 : What is Machine Learning
Lecture 1 Machine Learning Overview 00:17:04 Duration

Section 4 : Crash Course Overview
Lecture 1 Crash Course Section Introduction 00:01:06 Duration
Lecture 2 NumPy Crash Course 00:15:21 Duration
Lecture 3 Pandas Crash Course 00:04:15 Duration
Lecture 4 Data Visualization Crash Course 00:07:32 Duration
Lecture 5 SciKit Learn Preprocessing Overview 00:08:55 Duration
Lecture 6 Crash Course Review Exercise 00:02:02 Duration
Lecture 7 Crash Course Review Exercise - Solutions 00:05:53 Duration

Section 5 : Introduction to Neural Networks
Lecture 1 Introduction to Neural Networks 00:01:01 Duration
Lecture 2 Introduction to Perceptron 00:05:06 Duration
Lecture 3 Neural Network Activation Functions 00:06:22 Duration
Lecture 4 Cost Functions 00:03:34 Duration
Lecture 5 Gradient Descent Backpropagation 00:03:15 Duration
Lecture 6 TensorFlow Playground 00:08:43 Duration
Lecture 7 Manual Creation of Neural Network - Part One 00:06:09 Duration
Lecture 8 Manual Creation of Neural Network - Part Two - Operations 00:07:43 Duration
Lecture 9 Manual Creation of Neural Network - Part Three - Placeholders and Variables 00:08:47 Duration
Lecture 10 Manual Creation of Neural Network - Part Four - Session 00:09:34 Duration
Lecture 11 Manual Neural Network Classification Task 00:16:20 Duration

Section 6 : TensorFlow Basics
Lecture 1 Introduction to TensorFlow 00:01:18 Duration
Lecture 2 TensorFlow Basic Syntax 00:12:35 Duration
Lecture 3 TensorFlow Graphs 00:05:42 Duration
Lecture 4 Variables and Placeholders 00:05:50 Duration
Lecture 5 TensorFlow - A Neural Network - Part One 00:07:37 Duration
Lecture 6 TensorFlow - A Neural Network - Part Two 00:19:42 Duration
Lecture 7 TensorFlow Regression Example - Part One 00:19:23 Duration
Lecture 8 TensorFlow Regression Example _ Part Two 00:21:38 Duration
Lecture 9 TensorFlow Classification Example - Part One 00:13:52 Duration
Lecture 10 TensorFlow Classification Example - Part Two 00:12:36 Duration
Lecture 11 TF Regression Exercise 00:03:11 Duration
Lecture 12 TF Regression Exercise Solution Walkthrough
Lecture 13 TF Classification Exercise 00:04:18 Duration
Lecture 14 TF Classification Exercise Solution Walkthrough 00:11:21 Duration
Lecture 15 Saving and Restoring Models 00:05:49 Duration

Section 7 : Convolutional Neural Networks
Lecture 1 Introduction to Convolutional Neural Network Section 00:00:44 Duration
Lecture 2 Review of Neural Networks 00:02:26 Duration
Lecture 3 New Theory Topics
Lecture 4 Quick note on MNIST lecture
Lecture 5 MNIST Data Overview 00:04:40 Duration
Lecture 6 MNIST Basic Approach Part One 00:08:24 Duration
Lecture 7 MNIST Basic Approach Part Two 00:16:39 Duration
Lecture 8 CNN Theory Part One 00:18:36 Duration
Lecture 9 CNN Theory Part Two 00:04:27 Duration
Lecture 10 CNN MNIST Code Along - Part One 00:17:18 Duration
Lecture 11 CNN MNIST Code Along - Part Two 00:05:58 Duration
Lecture 12 Introduction to CNN Project 00:05:56 Duration
Lecture 13 CNN Project Exercise Solution - Part One 00:15:15 Duration
Lecture 14 CNN Project Exercise Solution - Part Two 00:12:50 Duration

Section 8 : Recurrent Neural Networks
Lecture 1 Introduction to RNN Section 00:01:00 Duration
Lecture 2 RNN Theory 00:07:51 Duration
Lecture 3 Manual Creation of RNN 00:11:45 Duration
Lecture 4 Vanishing Gradients 00:04:31 Duration
Lecture 5 Vanishing Gradients 00:04:30 Duration
Lecture 6 LSTM and GRU Theory 00:09:41 Duration
Lecture 7 Introduction to RNN with TensorFlow API 00:04:33 Duration
Lecture 8 RNN with TensorFlow - Part One 00:20:42 Duration
Lecture 9 RNN with TensorFlow - Part Two
Lecture 10 Quick Note on RNN Plotting Part 3
Lecture 11 RNN with TensorFlow - Part Three 00:07:44 Duration
Lecture 12 Time Series Exercise Overview 00:06:58 Duration
Lecture 13 Time Series Exercise Solution 00:18:08 Duration
Lecture 14 Quick Note on Word2Vec 00:02:43 Duration
Lecture 15 Word2Vec Theory 00:11:51 Duration
Lecture 16 Word2Vec Code Along - Part One 00:16:25 Duration
Lecture 17 Word2Vec Part Two 00:13:05 Duration

Section 9 : Miscellaneous Topics
Lecture 1 Intro to Miscellaneous Topics
Lecture 2 Deep Nets with Tensorflow Abstractions API - Part One 00:07:06 Duration
Lecture 3 Deep Nets with Tensorflow Abstractions API - Estimator API 00:07:18 Duration
Lecture 4 Deep Nets with Tensorflow Abstractions API - Keras 00:11:44 Duration
Lecture 5 Deep Nets with Tensorflow Abstractions API - Layers 00:10:35 Duration
Lecture 6 Tensorboard 00:15:59 Duration

Section 10 : AutoEncoders
Lecture 1 Autoencoder Basics 00:07:51 Duration
Lecture 2 Dimensionality Reduction with Linear Autoencoder 00:17:15 Duration
Lecture 3 Linear Autoencoder PCA Exercise Overview 00:01:38 Duration
Lecture 4 Linear Autoencoder PCA Exercise Solutions 00:07:44 Duration
Lecture 5 Stacked Autoencoder 00:19:25 Duration

Section 11 : Reinforcement Learning with OpenAI Gym
Lecture 1 Introduction to Reinforcement Learning with OpenAI Gym
Lecture 2 Extra Resources for Reinforcement Learning
Lecture 3 Introduction to OpenAI Gym 00:05:30 Duration
Lecture 4 OpenAI Gym Steup
Lecture 5 Open AI Gym Env Basics 00:05:35 Duration
Lecture 6 Open AI Gym Observations 00:07:59 Duration
Lecture 7 OpenAI Gym Actions 00:07:55 Duration
Lecture 8 Simple Neural Network Game 00:16:14 Duration
Lecture 9 Policy Gradient Theory 00:07:34 Duration
Lecture 10 Policy Gradient Code Along Part One 00:11:18 Duration
Lecture 11 Policy Gradient Code Along Part Two 00:12:16 Duration

Section 12 : GAN - Generative Adversarial Networks
Lecture 1 Introduction to GANs 00:07:06 Duration
Lecture 2 GAN Code Along - Part One 00:08:57 Duration
Lecture 3 GAN Code Along - Part Two 00:11:20 Duration
Lecture 4 GAN Code Along - Part Three 00:11:50 Duration