What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Key benefits

Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide

Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more

Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples

Description

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.

Who is this book for?

This book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial.

What you will learn

Understand the fundamentals of deep learning and how it is different from machine learning

Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning

Increase the predictive power of your model using feature engineering

Understand the basics of deep learning by solving a digit classification problem of MNIST

Demonstrate face generation based on the CelebA database, a promising application of generative models

Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

$38.99

$43.99

$43.99

Total $ 126.97

FAQs

How do I buy and download an eBook?

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

You may make copies of your eBook for your own use onto any machine
You may not pass copies of the eBook on to anyone else

How can I make a purchase on your website?

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

Register on our website using your email address and the password.
Search for the title by name or ISBN using the search option.
Select the title you want to purchase.
Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title.
Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)

Where can I access support around an eBook?

If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
To view your account details or to download a new copy of the book go to www.packtpub.com/account
To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us

What eBook formats do Packt support?

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks?

You can get the information you need immediately
You can easily take them with you on a laptop
You can download them an unlimited number of times
You can print them out
They are copy-paste enabled
They are searchable
There is no password protection
They are lower price than print
They save resources and space

What is an eBook?

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.

Lifeng Han Apr 02, 2018

This book has a very large coverage of content and knowledge. It covers the Data Modeling, Feature Engineering and Model Complexity, currently popular DL toolkit TensorFlow with examples, and different kinds of Neural Nets such as Feed-forward NN/CNN/GAN with some application introduction like classification task, sentiment analysis and face generation. It also introduces transfer learning and representation learning.I do believe the audiences will benefit from this advanced machine learning book with examples.

Amazon Verified review

Tom Francart Jul 17, 2018

While I was really looking forward to reading this book, I'm disappointed on two fronts:1. Typesetting: I normally wouldn't even mention this, but it's really bad. There are a lot of code examples, which are made very hard to read by improper line wrapping. Also, there is no syntax highlighting, which would have been helpful. In addition, the formulas look terrible: the font is not matched with the remainder of the text and the resolution is low.In addition, the figures are printed in black and white, but are not legible in black and white, for instance the text refers to "blue" and "red".On the whole, this book looks as if someone quickly converted it from another format and didn't even bother to look at the end result. This is unacceptable for a book produced by a supposedly professional company.2. Content: the references are unscientific; wikipedia and random websites are not reliable sources of information, especially for definitions that are essential to the book.Another disappointment is that the author describes how a certain DNN can be implemented in Tensorflow, but he does not explain how this DNN (in particular the hyperparameters) was obtained. I was hoping to learn by example how to find a good architecture and optimise the parameters, which is the hard part of deep learning.

Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks

What do you get with eBook?