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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