TensorFlow For Machine Intelligence · A hands-on introduction to learning algorithms by Abrahams, Sam -- Read -- Imperial Library of Trantor

Index

Preface

Welcome Background education

What you should expect to learn

This book’s layout

Section 1: Getting started with TensorFlow Section 2: TensorFlow and Machine Learning fundamentals Section 3: Implementing advanced deep models in TensorFlow Section 4: Additional tips, techniques, and features

Other machine learning libraries Further reading

I. Getting started with TensorFlow 1. Introduction

Data is everywhere Deep learning TensorFlow: a modern machine learning library TensorFlow: a technical overview

A brief history of deep learning at Google

What is TensorFlow?

Breaking down the one-sentence description

Open source: Library for numerical computation Data flow graphs

Beyond the one-sentence description

Distributed A suite of software

When to use TensorFlow TensorFlow’s strengths Challenges when using TensorFlow Onwards and upwards!

2. TensorFlow Installation

Selecting an installation environment Jupyter Notebook and Matplotlib Creating a Virtualenv environment Simple installation of TensorFlow Example installation from source: 64-bit Ubuntu Linux with GPU support

Installing dependencies Installing Bazel Installing CUDA Software (NVIDIA CUDA GPUs only) Building and Installing TensorFlow from Source

Installing Jupyter Notebook: Installing matplotlib Testing Out TensorFlow, Jupyter Notebook, and matplotlib Conclusion

II. TensorFlow and Machine Learning fundamentals 3. TensorFlow Fundamentals

Introduction to Computation Graphs

Graph basics Dependencies

Defining Computation Graphs in TensorFlow

Building your first TensorFlow graph Thinking with tensors

Python Native Types NumPy arrays

Tensor shape TensorFlow operations TensorFlow graphs TensorFlow Sessions

Fetches Feed dictionary

Adding Inputs with Placeholder nodes Variables

Creating variables Variable Initialization Changing Variables Trainable

Organizing your graph with name scopes Logging with TensorBoard Exercise: Putting it together

Building the graph Running the graph

Conclusion

4. Machine Learning Basics

Supervised learning introduction Saving training checkpoints Linear regression Logistic regression Softmax classification Multi-layer neural networks Gradient descent and backpropagation Conclusion

III. Implementing Advanced Deep Models in TensorFlow 5. Object Recognition and Classification

Convolutional Neural Networks Convolution

Input and Kernel Strides Padding Data Format Kernels in Depth

Common Layers

Convolution Layers

tf.nn.depthwise_conv2d tf.nn.separable_conv2d tf.nn.conv2d_transpose

Activation Functions

tf.nn.relu tf.sigmoid tf.tanh tf.nn.dropout

Pooling Layers

tf.nn.max_pool tf.nn.avg_pool

Normalization

tf.nn.local_response_normalization (tf.nn.lrn)

High Level Layers

tf.contrib.layers.convolution2d tf.contrib.layers.fully_connected Layer Input

Images and TensorFlow

Loading images Image Formats

JPEG and PNG TFRecord

Image Manipulation

Cropping Padding Flipping Saturation and Balance

Colors

Grayscale HSV RGB Lab Casting Images

CNN Implementation

Stanford Dogs Dataset Convert Images to TFRecords Load Images Model Training Debug the Filters with Tensorboard

Conclusion

6. Recurrent Neural Networks and Natural Language Processing

Introduction to Recurrent Networks

Approximating Arbitrary Programs Backpropagation Through Time Encoding and Decoding Sequences Implementing Our First Recurrent Network Vanishing and Exploding Gradients Long-Short Term Memory Architecture Variations

Word Vector Embeddings

Preparing the Wikipedia Corpus Model structure Noise Contrastive Classifier Training the model

Sequence Classification

Imdb Movie Review Dataset Using the Word Embeddings Sequence Labelling Model Softmax from last relevant activation Gradient clipping Training the model

Sequence Labelling

Optical Character Recognition Dataset Softmax shared between time steps Training the Model Bidirectional RNNs

Predictive coding

Character-level language modelling ArXiv abstracts API Preprocessing the data Predictive coding model Training the model Generating similiar sequences

Conclusion

IV. Additional Tips, Techniques, and Features 7. Deploying Models in Production

Setting up a Tensorflow serving development environment

Bazel workspace

Exporting trained models Defining a server interface Implementing an inference server The client app Preparing for production Conclusion

8. Helper Functions, Code Structure, and Classes

Ensure a directory structure Download function Disk caching decorator Attribute Dictionary Lazy property decorator Overwrite Graph Decorator

9. Conclusion

Next steps and additional resources

Read the docs Stay Updated Distributed TensorFlow Building New TensorFlow Functionality Get involved with the community Code from this book

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