There are a couple of things you can do to get the most out of this book. Firstly, it is recommended to at least have some basic knowledge of Python programming and machine learning.

Secondly, before proceeding to Chapter 3, Getting Started with Neural Networks and others, be sure to follow the setup instructions in Chapter 2, Getting Yourself Ready for Deep Learning. You will also be able to set up your own environment as long as you can practice the given examples.

Thirdly, familiarized yourself with TensorFlow and read its documentation. The TensorFlow documentation (https://www.tensorflow.org/api_docs/) is a great source of information and also contains a lot of great examples and important examples. You can also look around online, as there are various open source examples and deep-learning-related resources.

Fourthly, make sure you explore on your own. Try different settings or configurations for simple problems that don't require much computational time; this can help you to quickly get some ideas of how the model works and how to tune parameters.

Lastly, dive deeper into each type of model. This book explains the gist of various deep learning models in plain words while avoiding too much math; the goal is to help you understand the mechanisms of neural networks under the hood. While there are currently many different tools publicly available that provide high-level APIs, a good understanding of deep leaning will greatly help you to debug and improve model performance.

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

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

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

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Deep-Learning-Essentials. 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 a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/DeepLearningEssentials_ColorImages.pdf.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "In addition, alpha is the learning rate, vb is the bias of the visible layer, hb is the bias of the hidden layer, and W is the weight matrix. The sampling function sample_prob is the Gibbs-Sampling function and it decides which node to turn on."

A block of code is set as follows:

import mxnet as mx
tensor_cpu = mx.nd.zeros((100,), ctx=mx.cpu())
tensor_gpu = mx.nd.zeros((100,), ctx=mx.gpu(0))

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

$ sudo add-apt-repository ppa:graphics-drivers/ppa -y
$ sudo apt-get update
$ sudo apt-get install -y nvidia-375 nvidia-settings

Bold: Indicates a new term, an important word, or words that you see onscreen.