Introduction
- Introduction
- Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
- FAQ - Frequently Asked Questions
Installation and Setup
- Quick Note for MacOS and Linux Users
- Installing TensorFlow and Environment Setup
What is Machine Learning?
- Machine Learning Overview
Crash Course Overview
- Crash Course Section Introduction
- NumPy Crash Course
- Pandas Crash Course
- Data Visualization Crash Course
- SciKit Learn Preprocessing Overview
- Crash Course Review Exercise
- Crash Course Review Exercise - Solutions
Introduction to Neural Networks
- Introduction to Neural Networks
- Introduction to Perceptron
- Neural Network Activation Functions
- Cost Functions
- Gradient Descent Backpropagation
- TensorFlow Playground
- Manual Creation of Neural Network - Part One
- Manual Creation of Neural Network - Part Two - Operations
- Manual Creation of Neural Network - Part Three - Placeholders and Variables
- Manual Creation of Neural Network - Part Four - Session
- Manual Neural Network Classification Task
TensorFlow Basics
- Introduction to TensorFlow
- TensorFlow Basic Syntax
- TensorFlow Graphs
- Variables and Placeholders
- TensorFlow - A Neural Network - Part One
- TensorFlow - A Neural Network - Part Two
- TensorFlow Regression Example - Part One
- TensorFlow Regression Example _ Part Two
- TensorFlow Classification Example - Part One
- TensorFlow Classification Example - Part Two
- TF Regression Exercise
- TF Regression Exercise Solution Walkthrough
- TF Classification Exercise
- TF Classification Exercise Solution Walkthrough
- Saving and Restoring Models
Convolutional Neural Networks
- Introduction to Convolutional Neural Network Section
- Review of Neural Networks
- New Theory Topics
- Quick note on MNIST lecture
- MNIST Data Overview
- MNIST Basic Approach Part One
- MNIST Basic Approach Part Two
- CNN Theory Part One
- CNN Theory Part Two
- CNN MNIST Code Along - Part One
- CNN MNIST Code Along - Part Two
- Introduction to CNN Project
- CNN Project Exercise Solution - Part One
- CNN Project Exercise Solution - Part Two
Recurrent Neural Networks
- Introduction to RNN Section
- RNN Theory
- Manual Creation of RNN
- Vanishing Gradients
- LSTM and GRU Theory
- Introduction to RNN with TensorFlow API
- RNN with TensorFlow - Part One
- RNN with TensorFlow - Part Two
- Quick Note on RNN Plotting Part 3
- RNN with TensorFlow - Part Three
- Time Series Exercise Overview
- Time Series Exercise Solution
- Quick Note on Word2Vec
- Word2Vec Theory
- Word2Vec Code Along - Part One
- Word2Vec Part Two
Miscellaneous Topics
- Intro to Miscellaneous Topics
- Deep Nets with Tensorflow Abstractions API - Part One
- Deep Nets with Tensorflow Abstractions API - Estimator API
- Deep Nets with Tensorflow Abstractions API - Keras
- Deep Nets with Tensorflow Abstractions API - Layers
- Tensorboard
AutoEncoders
- Autoencoder Basics
- Dimensionality Reduction with Linear Autoencoder
- Linear Autoencoder PCA Exercise Overview
- Linear Autoencoder PCA Exercise Solutions
- Stacked Autoencoder
Reinforcement Learning with OpenAI Gym
- Introduction to Reinforcement Learning with OpenAI Gym
- Extra Resources for Reinforcement Learning
- Introduction to OpenAI Gym
- OpenAI Gym Steup
- Open AI Gym Env Basics
- Open AI Gym Observations
- OpenAI Gym Actions
- Simple Neural Network Game
- Policy Gradient Theory
