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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Deep Convolutional Neural Network (DCNN)

A Deep Convolutional Neural Network (DCNN) consists of many neural network layers. Two different types of layers, convolutional and pooling (that is, subsampling), are typically alternated. The depth of each filter increases from left to right in the network. The last stage is typically made of one or more fully connected layers:

Figure 1: An example of a DCNN

There are three key underlying concepts for convnets: local receptive fields, shared weights, and pooling. Let's review them together.

Local receptive fields

If we want to preserve the spatial information of an image or other form of data, then it is convenient to represent each image with a matrix of pixels. Given this, a simple way to encode the local structure is to connect a submatrix of adjacent input neurons into one single hidden neuron belonging to the next layer. That single hidden neuron represents one local receptive field. Note that this operation is named...