Book description
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices
Key Features
 Introduces and then uses TensorFlow 2 and Keras right from the start
 Teaches key machine and deep learning techniques
 Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
Book Description
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.
What you will learn
 Build machine learning and deep learning systems with TensorFlow 2 and the Keras API
 Use Regression analysis, the most popular approach to machine learning
 Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers
 Use GANs (generative adversarial networks) to create new data that fits with existing patterns
 Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another
 Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response
 Train your models on the cloud and put TF to work in real environments
 Explore how Google tools can automate simple ML workflows without the need for complex modeling
Who this book is for
This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.
Publisher resources
Table of contents
 Preface

Neural Network Foundations with TensorFlow 2.0
 What is TensorFlow (TF)?
 What is Keras?
 What are the most important changes in TensorFlow 2.0?
 Introduction to neural networks
 Perceptron
 Multilayer perceptron – our first example of a network

A real example – recognizing handwritten digits
 Onehot encoding (OHE)
 Defining a simple neural network in TensorFlow 2.0
 Running a simple TensorFlow 2.0 net and establishing a baseline
 Improving the simple net in TensorFlow 2.0 with hidden layers
 Further improving the simple net in TensorFlow with Dropout
 Testing different optimizers in TensorFlow 2.0
 Increasing the number of epochs
 Controlling the optimizer learning rate
 Increasing the number of internal hidden neurons
 Increasing the size of batch computation
 Summarizing experiments run for recognizing handwritten charts
 Regularization
 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
 References

TensorFlow 1.x and 2.x
 Understanding TensorFlow 1.x

Understanding TensorFlow 2.x
 Eager execution
 AutoGraph
 Keras APIs – three programming models
 Callbacks
 Saving a model and weights
 Training from tf.data.datasets
 tf.keras or Estimators?
 Ragged tensors
 Custom training
 Distributed training in TensorFlow 2.x
 Changes in namespaces
 Converting from 1.x to 2.x
 Using TensorFlow 2.x effectively
 The TensorFlow 2.x ecosystem
 Keras or tf.keras?
 Summary
 Regression
 Convolutional Neural Networks

Advanced Convolutional Neural Networks

Computer vision
 Composing CNNs for complex tasks
 Classifying FashionMNIST with a tf.keras  estimator model
 Run FashionMNIST the tf.keras  estimator model on GPUs
 Deep Inceptionv3 Net used for transfer learning
 Transfer learning for classifying horses and humans
 Application Zoos with tf.keras and TensorFlow Hub
 Other CNN architectures
 Answering questions about images (VQA)
 Style transfer
 Creating a DeepDream network
 Inspecting what a network has learned
 Video
 Textual documents
 Audio and music
 A summary of convolution operations
 Capsule networks
 Summary
 References

Computer vision
 Generative Adversarial Networks

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 modelbased embeddings
 Summary
 References
 Recurrent Neural Networks
 Autoencoders
 Unsupervised Learning
 Reinforcement Learning
 TensorFlow and Cloud
 TensorFlow for Mobile and IoT and TensorFlow.js
 An introduction to AutoML

The Math Behind Deep Learning
 History
 Some mathematical tools
 Activation functions
 Backpropagation
 Thinking about backpropagation and convnets
 Thinking about backpropagation and RNNs
 A note on TensorFlow and automatic differentiation
 Summary
 References
 Tensor Processing Unit
 Other Books You May Enjoy
 Index
Product information
 Title: Deep Learning with TensorFlow 2 and Keras  Second Edition
 Author(s):
 Release date: December 2019
 Publisher(s): Packt Publishing
 ISBN: 9781838823412
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