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