Dan Van Boxel - Hands-On Deep Learning with Tensorflow
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This book is your guide to exploring the possibilities in the field of deep learning, making use of Googles TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data.
About This Book
- Want to learn what more can be done with deep learning? Explore various neural networks with the help of this comprehensive guide
- Rich in concepts, advanced guide on deep learning that will give you background to innovate in your environment
Who This Book Is For
If you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise in working with convoluted neural networks, then this book is for you. Some familiarity with C++ or Python is assumed.
What You Will Learn
- Set up your computing environment and install TensorFlow
- Build simple TensorFlow graphs for everyday computations
- Apply logistic regression for classification with TensorFlow
- Design and train a multilayer neural network with TensorFlow
- Intuitively understand convolutional neural networks for image recognition
- Bootstrap a neural network from simple to more accurate models
- See how to use TensorFlow with other types of networks
- Program networks with SciKit-Flow, a high-level interface to TensorFlow
In Detail
Dan Van Boxels Deep Learning with TensorFlow is based on Dans best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.
With Dans guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data.
In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.Style and Approach
This book is your go-to guide to becoming a deep learning expert in your organization. Dan helps you evaluate common and not-so-common deep neural networks with the help of insightful examples that you can relate to, and show how they can be exploited in the real world with complex raw data.
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Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2017
Production reference: 1280717
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78728-277-3
www.packtpub.com
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Dan Van Boxel
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Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for Dan Does Data, a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research articles and presented findings at the Transportation Research Board and other academic journals.
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TensorFlow is an open source software library for machine learning and training neural networks. TensorFlow was originally developed by Google, and was made open source in 2015.
Over the course of this book, you will learn how to use TensorFlow to solve a novel research problem. You'll use one of the most popular machine learning approaches, neural networks with TensorFlow. We'll work on both the simple and deep neural networks to improve our models.
You'll study images of letters and digits in various fonts with the goal of identifying fonts based on one specific image of a single letter. This will be a straightforward classification problem.
As no single pixel or positionbut local structures among pixelsis important, it's an ideal problem for deep learning with TensorFlow. Though we'll start with simple models, this series will gradually introduce more nuanced approaches and explain the code line by line. By the end of this book, you'll have created your own advanced model for font recognition.
So let's put on our helmets; we're going deep into data mines with TensorFlow.
, Getting Started, discusses the techniques and the models we'll apply using TensorFlow. In this chapter, we will install TensorFlow on a machine we can use. After some small steps with basic computations, we will jump into a machine learning problem, successfully building a decent model with just logistic regression and a few lines of TensorFlow code.
, Deep Neural Networks , shows TensorFlow in its prime with deep neural networks. You will learn about the single and multiple hidden layer model. You will also learn about the different types of neural networks and build and train our first neural network with TensorFlow.
, Convolutional Neural Networks, talks about the most powerful developments in deep learning and applies the concepts of convolution to a simple example in TensorFlow. We will tackle the practical aspects of understanding convolution. We will explain what a convolutional and pooling layer is in a neural net, following with a TensorFlow example.
, Introducing Recurrent Neural Networks , introduces the concept of RNN models, and their implementation in TensorFlow. We will look at a simple interface to TensorFlow called TensorFlow learn. We will also walk through dense neural networks as well as understand convolutional neural networks and extracting weights in detail.
, Wrapping Up , wraps up our look at TensorFlow. We'll revisit our TensorFlow models for font classification, and review their accuracy.
While this book will show you how to install TensorFlow, there are a few dependencies you need to be aware of. At a minimum, you need a recent version of Python 2 or 3 and NumPy. To get the most out of the book, you should also have Matplotlib and IPython.
With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel is your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.
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