Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Jupyter Notebook Python
Latest commit 905bd82 Dec 16, 2016 @BinRoot committed on GitHub Created LICENCE

README.md

Machine Learning with TensorFlow

This is the official code repository for Machine Learning with TensorFlow.

⚠️ Warning: The book will be released in a month or two, so this repo is a pre-release of the entire code. I will be heavily updating this repo in the coming weeks. Stay tuned, and follow along! :)

Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

Summary

Chapter 2 - TensorFlow Basics

  • Concept 1: Defining tensors
  • Concept 2: Evaluating ops
  • Concept 3: Interactive session
  • Concept 4: Session loggings
  • Concept 5: Variables
  • Concept 6: Saving variables
  • Concept 7: Loading variables
  • Concept 8: TensorBoard

Chapter 3 - Regression

  • Concept 1: Linear regression
  • Concept 2: Polynomial regression
  • Concept 3: Regularization

Chapter 4 - Classification

  • Concept 1: Linear regression for classification
  • Concept 2: Logistic regression
  • Concept 3: 2D Logistic regression
  • Concept 4: Softmax classification

Chapter 5 - Clustering

  • Concept 1: Clustering
  • Concept 2: Segmentation
  • Concept 3: Self-organizing map

Chapter 6 - Hidden markov models

  • Concept 1: Forward algorithm
  • Concept 2: Viterbi decode

Chapter 7 - Autoencoders

  • Concept 1: Autoencoder
  • Concept 2: Applying an autoencoder to images
  • Concept 3: Denoising autoencoder

Chapter 8 - Reinforcement learning

  • Concept 1: Reinforcement learning

Chapter 9 - Convolutional Neural Networks

  • Concept 1: Using CIFAR-10 dataset
  • Concept 2: Convolutions
  • Concept 3: Convolutional neural network

Chapter 10 - Recurrent Neural Network

  • Concept 1: Loading timeseries data
  • Concept 2: Recurrent neural networks
  • Concept 3: Applying RNN to real-world data for timeseries prediction