Book description
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide
About This Book
 Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
 Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
 Realworld contextualization through some deep learning problems concerning research and application
Who This Book Is For
The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.
What You Will Learn
 Learn about machine learning landscapes along with the historical development and progress of deep learning
 Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x
 Access public datasets and utilize them using TensorFlow to load, process, and transform data
 Use TensorFlow on realworld datasets, including images, text, and more
 Learn how to evaluate the performance of your deep learning models
 Using deep learning for scalable object detection and mobile computing
 Train machines quickly to learn from data by exploring reinforcement learning techniques
 Explore active areas of deep learning research and applications
In Detail
Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.
Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.
After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Style and approach
This stepbystep guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
Publisher resources
Table of contents
 Preface
 Getting Started with Deep Learning

First Look at TensorFlow
 General overview
 Installing TensorFlow on Linux
 Requirements for running TensorFlow with GPU from NVIDIA
 How to install TensorFlow
 Installing TensorFlow on Windows
 Computational graphs
 Why a computational graph?
 The programming model
 Data model
 TensorBoard
 Implementing a single input neuron
 Source code for the single input neuron
 Migrating to TensorFlow 1.x
 Summary
 Using TensorFlow on a FeedForward Neural Network
 TensorFlow on a Convolutional Neural Network
 Optimizing TensorFlow Autoencoders
 Recurrent Neural Networks
 GPU Computing
 Advanced TensorFlow Programming
 Advanced Multimedia Programming with TensorFlow
 Reinforcement Learning
Product information
 Title: Deep Learning with TensorFlow
 Author(s):
 Release date: April 2017
 Publisher(s): Packt Publishing
 ISBN: 9781786469786
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