Learning TensorFlow. A Guide to Building Deep Learning Systems

Spis treści ebooka

• Preface
• 1. Introduction
  • Going Deep
    • Using TensorFlow for AI Systems
      • Pre-trained models: state-of-the-art computer vision for all
      • Generating rich natural language descriptions for images
      • Text summarization
  • TensorFlow: Whats in a Name?
  • A High-Level Overview
  • Summary
• 2. Go with the Flow: Up and Running with TensorFlow
  • Installing TensorFlow
  • Hello World
  • MNIST
  • Softmax Regression
  • Summary
• 3. Understanding TensorFlow Basics
  • Computation Graphs
    • What Is a Computation Graph?
    • The Benefits of Graph Computations
  • Graphs, Sessions, and Fetches
    • Creating a Graph
    • Creating a Session and Running It
    • Constructing and Managing Our Graph
    • Fetches
  • Flowing Tensors
    • Nodes Are Operations, Edges Are Tensor Objects
      • Setting attributes with source operations
    • Data Types
      • Casting
    • Tensor Arrays and Shapes
      • Matrix multiplication
    • Names
      • Name scopes
  • Variables, Placeholders, and Simple Optimization
    • Variables
    • Placeholders
    • Optimization
      • Training to predict
      • Defining a loss function
      • MSE and cross entropy
      • The gradient descent optimizer
      • Sampling methods
      • Gradient descent in TensorFlow
      • Wrapping it up with examples
        • Example 1: linear regression
        • Example 2: logistic regression
  • Summary
• 4. Convolutional Neural Networks
  • Introduction to CNNs
  • MNIST: Take II
    • Convolution
    • Pooling
    • Dropout
    • The Model
  • CIFAR10
    • Loading the CIFAR10 Dataset
    • Simple CIFAR10 Models
  • Summary
• 5. Text I: Working with Text and Sequences, and TensorBoard Visualization
  • The Importance of Sequence Data
  • Introduction to Recurrent Neural Networks
    • Vanilla RNN Implementation
      • MNIST images as sequences
      • The RNN step
      • Applying the RNN step with tf.scan()
      • Sequential outputs
      • RNN classification
      • Visualizing the model with TensorBoard
    • TensorFlow Built-in RNN Functions
      • tf.contrib.rnn.BasicRNNCell and tf.nn.dynamic_rnn()
  • RNN for Text Sequences
    • Text Sequences
    • Supervised Word Embeddings
    • LSTM and Using Sequence Length
    • Training Embeddings and the LSTM Classifier
      • Stacking multiple LSTMs
  • Summary
• 6. Text II: Word Vectors, Advanced RNN, and Embedding Visualization
  • Introduction to Word Embeddings
  • Word2vec
    • Skip-Grams
    • Embeddings in TensorFlow
    • The Noise-Contrastive Estimation (NCE) Loss Function
    • Learning Rate Decay
    • Training and Visualizing with TensorBoard
    • Checking Out Our Embeddings
  • Pretrained Embeddings, Advanced RNN
    • Pretrained Word Embeddings
    • Bidirectional RNN and GRU Cells
  • Summary
• 7. TensorFlow Abstractions and Simplifications
  • Chapter Overview
    • High-Level Survey
  • contrib.learn
    • Linear Regression
    • DNN Classifier
    • FeatureColumn
    • Homemade CNN with contrib.learn
  • TFLearn
    • Installation
    • CNN
    • RNN
    • Keras
      • Installation
      • Sequential model
      • Functional model
      • Autoencoders
    • Pretrained models with TF-Slim
      • TF-Slim
      • Creating CNN models with TF-Slim
      • Downloading and using a pretrained model
  • Summary
• 8. Queues, Threads, and Reading Data
  • The Input Pipeline
  • TFRecords
    • Writing with TFRecordWriter
  • Queues
    • Enqueuing and Dequeuing
    • Multithreading
    • Coordinator and QueueRunner
      • tf.train.Coordinator
      • tf.train.QueueRunner and tf.RandomShuffleQueue
  • A Full Multithreaded Input Pipeline
    • tf.train.string_input_producer() and tf.TFRecordReader()
    • tf.train.shuffle_batch()
    • tf.train.start_queue_runners() and Wrapping Up
  • Summary
• 9. Distributed TensorFlow
  • Distributed Computing
    • Where Does the Parallelization Take Place?
    • What Is the Goal of Parallelization?
  • TensorFlow Elements
    • tf.app.flags
    • Clusters and Servers
    • Replicating a Computational Graph Across Devices
    • Managed Sessions
    • Device Placement
  • Distributed Example
  • Summary
• 10. Exporting and Serving Models with TensorFlow
  • Saving and Exporting Our Model
    • Assigning Loaded Weights
    • The Saver Class
  • Introduction to TensorFlow Serving
    • Overview
    • Installation
      • Installing Serving
    • Building and Exporting
      • Exporting our model
  • Summary
• A. Tips on Model Construction and Using TensorFlow Serving
  • Model Structuring and Customization
    • Model Structuring
      • Modular design
      • Variable sharing
      • Class encapsulation
    • Customization
      • Homemade loss functions
      • Regularization
      • Writing your very own op
  • Required and Recommended Components for TensorFlow Serving
    • What Is a Docker Container and Why Do We Use It?
    • Some Basic Docker Commands
• Index

pokaż cały spis treści

ukryj recenzje