Deep Learning with TensorFlow - Second Edition电子书

Deep Learning with TensorFlow - Second Edition

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Preface

Who this book is for

What this book covers

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Download the example code files

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Conventions used

Note

Tip

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Chapter 1. Getting Started with Deep Learning

A soft introduction to machine learning

Note

Supervised learning

Unbalanced data

Unsupervised learning

Reinforcement learning

What is deep learning?

Artificial neural networks

The biological neurons

The artificial neuron

Note

How does an ANN learn?

ANNs and the backpropagation algorithm

Weight optimization

Stochastic gradient descent

Neural network architectures

Deep Neural Networks (DNNs)

Multilayer perceptron

Deep Belief Networks (DBNs)

Convolutional Neural Networks (CNNs)

AutoEncoders

Recurrent Neural Networks (RNNs)

Emergent architectures

Deep learning frameworks

Summary

Chapter 2. A First Look at TensorFlow

A general overview of TensorFlow

What's new from TensorFlow v1.6 forwards?

Nvidia GPU support optimized

Introducing TensorFlow Lite

Eager execution

Optimized Accelerated Linear Algebra (XLA)

Installing and configuring TensorFlow

TensorFlow computational graph

TensorFlow code structure

Tip

Tip

Note

Eager execution with TensorFlow

Data model in TensorFlow

Tensor

Tip

Rank and shape

Data type

Tip

Note

Variables

Fetches

Feeds and placeholders

Tip

Visualizing computations through TensorBoard

How does TensorBoard work?

Note

Linear regression and beyond

Tip

Linear regression revisited for a real dataset

Tip

Summary

Chapter 3. Feed-Forward Neural Networks with TensorFlow

Feed-forward neural networks (FFNNs)

Feed-forward and backpropagation

Weights and biases

Tip

Note

Activation functions

Using sigmoid

Using tanh

Using ReLU

Using softmax

Implementing a feed-forward neural network

Exploring the MNIST dataset

Softmax classifier

Implementing a multilayer perceptron (MLP)

Training an MLP

Using MLPs

Dataset description

Preprocessing

A TensorFlow implementation of MLP for client-subscription assessment

Deep Belief Networks (DBNs)

Restricted Boltzmann Machines (RBMs)

Construction of a simple DBN

Unsupervised pre-training

Supervised fine-tuning

Implementing a DBN with TensorFlow for client-subscription assessment

Tip

Tuning hyperparameters and advanced FFNNs

Tuning FFNN hyperparameters

Number of hidden layers

Number of neurons per hidden layer

Weight and biases initialization

Selecting the most suitable optimizer

GridSearch and randomized search for hyperparameters tuning

Regularization

Dropout optimization

Summary

Chapter 4. Convolutional Neural Networks

Main concepts of CNNs

CNNs in action

LeNet5

Note

Implementing a LeNet-5 step by step

Note

Note

AlexNet

Note

Transfer learning

Pretrained AlexNet

Dataset preparation

Fine-tuning implementation

VGG

Artistic style learning with VGG-19

Input images

Content extractor and loss

Style extractor and loss

Merger and total loss

Training

Inception-v3

Exploring Inception with TensorFlow

Emotion recognition with CNNs

Note

Testing the model on your own image

Source code

Summary

Chapter 5. Optimizing TensorFlow Autoencoders

How does an autoencoder work?

Implementing autoencoders with TensorFlow

Improving autoencoder robustness

Implementing a denoising autoencoder

Implementing a convolutional autoencoder

Encoder

Decoder

Fraud analytics with autoencoders

Description of the dataset

Problem description

Exploratory data analysis

Tip

Training, validation, and testing set preparation

Normalization

Autoencoder as an unsupervised feature learning algorithm

Note

Evaluating the model

Summary

Chapter 6. Recurrent Neural Networks

Working principles of RNNs

Implementing basic RNNs in TensorFlow

RNN and the long-term dependency problem

Bi-directional RNNs

RNN and the gradient vanishing-exploding problem

LSTM networks

GRU cell

Implementing an RNN for spam prediction

Data description and preprocessing

Developing a predictive model for time series data

Description of the dataset

Pre-processing and exploratory analysis

LSTM predictive model

Model evaluation

An LSTM predictive model for sentiment analysis

Network design

LSTM model training

Visualizing through TensorBoard

LSTM model evaluation

Note

Human activity recognition using LSTM model

Dataset description

Workflow of the LSTM model for HAR

Implementing an LSTM model for HAR

Summary

Chapter 7. Heterogeneous and Distributed Computing

GPGPU computing

The GPGPU history

The CUDA architecture

The GPU programming model

The TensorFlow GPU setup

Note

Update TensorFlow

GPU representation

Using a GPU

GPU memory management

Assigning a single GPU on a multi-GPU system

The source code for GPU with soft placement

Using multiple GPUs

Distributed computing

Model parallelism

Data parallelism

The distributed TensorFlow setup

Summary

Chapter 8. Advanced TensorFlow Programming

tf.estimator

Estimators

Graph actions

Parsing resources

Flower predictions

TFLearn

Installation

Titanic survival predictor

PrettyTensor

Chaining layers

Normal mode

Sequential mode

Branch and join

Digit classifier

Keras

Keras programming models

Sequential model

Sentiment classification of movie reviews

Functional API

SqueezeNet

Summary

Chapter 9. Recommendation Systems Using Factorization Machines

Recommendation systems

Collaborative filtering approaches

Content-based filtering approaches

Hybrid recommender systems

Model-based collaborative filtering

Movie recommendation using collaborative filtering

The utility matrix

Note

Description of the dataset

Ratings data

Movies data

Users data

Exploratory analysis of the MovieLens dataset

Implementing a movie RE

Training the model with the available ratings

Inferencing the saved model

Generating the user-item table

Clustering similar movies

Tip

Movie rating prediction by users

Finding top k movies

Predicting top k similar movies

Computing user-user similarity

Evaluating the recommender system

Factorization machines for recommendation systems

Factorization machines

Cold-start problem and collaborative-filtering approaches

Problem definition and formulation

Dataset description

Workflow of the implementation

Preprocessing

Training the FM model

Improved factorization machines

Neural factorization machines

Dataset description

Using NFM for the movie recommendation

Model training

Model evaluation

Note

Summary

Chapter 10. Reinforcement Learning

The RL problem

OpenAI Gym

OpenAI environments

The env class

Installing and running OpenAI Gym

The Q-Learning algorithm

The FrozenLake environment

Deep Q-learning

Deep Q neural networks

The Cart-Pole problem

Deep Q-Network for the Cart-Pole problem

The Experience Replay method

Exploitation and exploration

The Deep Q-Learning training algorithm

Summary

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