Book description
Take the next step in implementing various common and notsocommon neural networks with Tensorflow 1.x
About This Book
 Skill up and implement tricky neural networks using Google's TensorFlow 1.x
 An easytofollow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.
 Handson recipes to work with Tensorflow on desktop, mobile, and cloud environment
Who This Book Is For
This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful.
What You Will Learn
 Install TensorFlow and use it for CPU and GPU operations
 Implement DNNs and apply them to solve different AIdriven problems.
 Leverage different data sets such as MNIST, CIFAR10, and Youtube8m with TensorFlow and learn how to access and use them in your code.
 Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box.
 Use different regression techniques for prediction and classification problems
 Build single and multilayer perceptrons in TensorFlow
 Implement CNN and RNN in TensorFlow, and use it to solve realworld use cases.
 Learn how restricted Boltzmann Machines can be used to recommend movies.
 Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection.
 Master the different reinforcement learning methods to implement game playing agents.
 GANs and their implementation using TensorFlow.
In Detail
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipebased guide will take you from the realm of DNN theory to implementing them practically to solve the reallife problems in artificial intelligence domain.
In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Qlearning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow.
With a problemsolution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future.
By using crisp, nononsense recipes, you will become an expert in implementing deep learning techniques in growing realworld applications and research areas such as reinforcement learning, GANs, autoencoders and more.
Style and approach
This book consists of handson recipes where you'll deal with realworld problems.
You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x.
Your onestop solution for common and notsocommon pain points, this is a book that you must have on the shelf.
Publisher resources
Table of contents
 Preface

TensorFlow  An Introduction
 Introduction
 Installing TensorFlow
 Hello world in TensorFlow
 Understanding the TensorFlow program structure
 Working with constants, variables, and placeholders
 Performing matrix manipulations using TensorFlow
 Using a data flow graph
 Migrating from 0.x to 1.x
 Using XLA to enhance computational performance
 Invoking CPU/GPU devices
 TensorFlow for Deep Learning
 Different Python packages required for DNNbased problems
 Regression
 Neural Networks  Perceptron
 Convolutional Neural Networks

Advanced Convolutional Neural Networks
 Introduction
 Creating a ConvNet for Sentiment Analysis
 Inspecting what filters a VGG prebuilt network has learned
 Classifying images with VGGNet, ResNet, Inception, and Xception
 Recycling prebuilt Deep Learning models for extracting features
 Very deep InceptionV3 Net used for Transfer Learning
 Generating music with dilated ConvNets, WaveNet, and NSynth
 Answering questions about images (Visual Q&A)
 Classifying videos with pretrained nets in six different ways

Recurrent Neural Networks
 Introduction
 Neural machine translation  training a seq2seq RNN
 Neural machine translation  inference on a seq2seq RNN
 All you need is attention  another example of a seq2seq RNN
 Learning to write as Shakespeare with RNNs
 Learning to predict future Bitcoin value with RNNs
 Manytoone and manytomany RNN examples
 Unsupervised Learning
 Autoencoders
 Reinforcement Learning

Mobile Computation
 Introduction
 Installing TensorFlow mobile for macOS and Android
 Playing with TensorFlow and Android examples
 Installing TensorFlow mobile for macOS and iPhone
 Optimizing a TensorFlow graph for mobile devices
 Profiling a TensorFlow graph for mobile devices
 Transforming a TensorFlow graph for mobile devices
 Generative Models and CapsNet

Distributed TensorFlow and Cloud Deep Learning
 Introduction
 Working with TensorFlow and GPUs
 Playing with Distributed TensorFlow: multiple GPUs and one CPU
 Playing with Distributed TensorFlow: multiple servers
 Training a Distributed TensorFlow MNIST classifier
 Working with TensorFlow Serving and Docker
 Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
 Running Distributed TensorFlow on Google CloudML
 Running Distributed TensorFlow on Microsoft Azure
 Running Distributed TensorFlow on Amazon AWS
 Learning to Learn with AutoML (MetaLearning)
 TensorFlow Processing Units
Product information
 Title: TensorFlow 1.x Deep Learning Cookbook
 Author(s):
 Release date: December 2017
 Publisher(s): Packt Publishing
 ISBN: 9781788293594
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