Machine Learning with TensorFlow

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

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

Chapter 11 - Seq2Seq Model

• Concept 1: Multi-cell RNN
• Concept 2: Embedding lookup
• Concept 3: Seq2seq model

Chapter 12 - Ranking

• Concept 1: RankNet
• Concept 2: Image embedding
• Concept 3: Image ranking