Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal

Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

Preface

Machine learning, artificial intelligence, and the deep learning Cambrian explosion

Who this book is for

What this book covers

What you need for this book

Free Chapter

Neural Network Foundations with TensorFlow 2.0

Neural Network Foundations with TensorFlow 2.0

What is TensorFlow (TF)?

What are the most important changes in TensorFlow 2.0?

Introduction to neural networks

Multi-layer perceptron – our first example of a network

A real example – recognizing handwritten digits

Playing with Google Colab – CPUs, GPUs, and TPUs

Sentiment analysis

Hyperparameter tuning and AutoML

Predicting output

A practical overview of backpropagation

What have we learned so far?

Towards a deep learning approach

TensorFlow 1.x and 2.x

TensorFlow 1.x and 2.x

Understanding TensorFlow 1.x

Understanding TensorFlow 2.x

The TensorFlow 2.x ecosystem

Keras or tf.keras?

Regression

What is regression?

Prediction using linear regression

TensorFlow Estimators

Predicting house price using linear regression

Classification tasks and decision boundaries

Convolutional Neural Networks

Convolutional Neural Networks

Deep Convolutional Neural Network (DCNN)

An example of DCNN ‒ LeNet

Recognizing CIFAR-10 images with deep learning

Very deep convolutional networks for large-scale image recognition

Advanced Convolutional Neural Networks

Advanced Convolutional Neural Networks

Computer vision

Textual documents

Audio and music

A summary of convolution operations

Capsule networks

Generative Adversarial Networks

Generative Adversarial Networks

Deep convolutional GAN (DCGAN)

Some interesting GAN architectures

Cool applications of GANs

CycleGAN in TensorFlow 2.0

Word Embeddings

Word Embeddings

Word embedding ‒ origins and fundamentals

Distributed representations

Static embeddings

Creating your own embedding using gensim

Exploring the embedding space with gensim

Using word embeddings for spam detection

Neural embeddings – not just for words

Character and subword embeddings

Dynamic embeddings

Sentence and paragraph embeddings

Language model-based embeddings

Recurrent Neural Networks

Recurrent Neural Networks

The basic RNN cell

RNN cell variants

Encoder-Decoder architecture – seq2seq

Attention mechanism

Transformer architecture

Autoencoders

Introduction to autoencoders

Vanilla autoencoders

Sparse autoencoder

Denoising autoencoders

Stacked autoencoder

Unsupervised Learning

Unsupervised Learning

Principal component analysis

Self-organizing maps

Restricted Boltzmann machines

Variational Autoencoders

Reinforcement Learning

Reinforcement Learning

Introduction to OpenAI Gym

Deep Q-Networks

Deep deterministic policy gradient

TensorFlow and Cloud

TensorFlow and Cloud

Deep learning on cloud

Virtual machines on cloud

Jupyter Notebooks on cloud

TensorFlow Extended for production

TensorFlow Enterprise

TensorFlow for Mobile and IoT and TensorFlow.js

TensorFlow for Mobile and IoT and TensorFlow.js

TensorFlow Mobile

TensorFlow Lite

Pretrained models in TensorFlow Lite

An overview of federated learning at the edge

An introduction to AutoML

An introduction to AutoML

What is AutoML?

Achieving AutoML

Automatic data preparation

Automatic feature engineering

Automatic model generation

Google Cloud AutoML

Bringing Google AutoML to Kaggle

The Math Behind Deep Learning

The Math Behind Deep Learning

Some mathematical tools

Activation functions

Backpropagation

Thinking about backpropagation and convnets

Thinking about backpropagation and RNNs

A note on TensorFlow and automatic differentiation

Tensor Processing Unit

Tensor Processing Unit

C/G/T processing units

Three generations of TPUs and Edge TPU

TPU performance

How to use TPUs with Colab

Using pretrained TPU models

Using TensorFlow 2.1 and nightly build

Other Books You May Enjoy

Other Books You May Enjoy

Index

Customer Reviews

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What is TensorFlow (TF)?

TensorFlow is a powerful open source software library developed by the Google Brain team for deep neural networks, the topic covered in this book. It was first made available under the Apache 2.0 License in November 2015 and has since grown rapidly; as of May 2019, its GitHub repository (https://github.com/tensorflow/tensorflow) has more than 51,000 commits, with roughly 1,830 contributors. This in itself provides a measure of the popularity of TensorFlow.

Let us first learn what exactly TensorFlow is and why it is so popular among deep neural network researchers and engineers. Google calls it "an open source software library for machine intelligence," but since there are so many other deep learning libraries like PyTorch (https://pytorch.org/), Caffe (https://caffe.berkeleyvision.org/), and MxNet (https://mxnet.apache.org/), what makes TensorFlow special? Most other deep learning libraries – like TensorFlow – have auto-differentiation (a useful mathematical tool used for optimization), many are open source platforms, most of them support the CPU/GPU option, have pretrained models, and support commonly used NN architectures like recurrent neural networks, convolutional neural networks, and deep belief networks.

So, what else is there in TensorFlow? Let me list the top features:

It works with all popular languages such as Python, C++, Java, R, and Go.
Keras – a high-level neural network API that has been integrated with TensorFlow (in 2.0, Keras became the standard API for interacting with TensorFlow). This API specifies how software components should interact.
TensorFlow allows model deployment and ease of use in production.
Support for eager computation (see Chapter 2, TensorFlow 1.x and 2.x) has been introduced in TensorFlow 2.0, in addition to graph computation based on static graphs.
Most importantly, TensorFlow has very good community support.

The number of stars on GitHub (see Figure 1) is a measure of popularity for all open source projects. As of March 2019, TensorFlow, Keras, and PyTorch have 123,000, 39,000, and 25,000 stars respectively, which makes TensorFlow the most popular framework for machine learning:

Figure 1: Number of stars for various deep learning projects on GitHub

Google Trends is another measure of popularity, and again TensorFlow and Keras are the two top frameworks (late 2019), with PyTorch rapidly catching up (see Figure 2).

Figure 2: Google Trends for various deep learning projects