Chapter 1, 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.

Chapter 2, 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.

Chapter 3, 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.

Chapter 4, 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.

Chapter 5, 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.

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The first thing you need to do is download the source code pack for this book and open the simple.py file."

A block of code is set as follows:

import tensorflow as tf
# You can create constants in TF to hold specific values
a = tf.constant(1)
b = tf.constant(2)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import tensorflow as tf
# You can create constants in TF to hold specific values
a = tf.constant(1)
b = tf.constant(2)

Any command-line input or output is written as follows:

sudo pip3 install ./tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Click on +New to create a new file. Here we'll create a Jupyter notebook".

Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or may have disliked. Reader feedback is important for us to develop titles that you really get the most out of.

To send us general feedback, simply send an email to <[email protected]>, and mention the book title via the subject of your message.

If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide on www.packtpub.com/authors.

Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.

You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

You can download the code files by following these steps:

You can also download the code files by clicking on the Code Files button on the book's webpage at the Packt Publishing website. This page can be accessed by entering the book's name in the Search box. Please note that you need to be logged in to your Packt account.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Deep-Learning-with-TensorFlow. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide you with a PDF file that has color images of the screenshots/diagrams used in this book. The color images will help you better understand the changes in the output. You can download this file from https://www.packtpub.com/sites/default/files/HandsOnDeepLearningwithTensorFlow.pdf.

Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books—maybe a mistake in the text or the code—we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visiting http://www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.

To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.

Piracy of copyrighted material on the Internet is an ongoing problem across all media. At Packt, we take the protection of our copyright and licenses very seriously. If you come across any illegal copies of our works in any form on the Internet, please provide us with the location address or website name immediately so that we can pursue a remedy.

Please contact us at <[email protected]> with a link to the suspected pirated material.

We appreciate your help in protecting our authors and our ability to bring you valuable content.

If you have a problem with any aspect of this book, you can contact us at <[email protected]>, and we will do our best to address the problem.