To get the most out of this book

This book does not assume any specialist knowledge, only solid general computer skills. Python is a relatively easy (and incredibly useful!) language to learn, so don't worry if you have limited or no programming background.

The book does contain some relatively simple mathematics, and some theory, that some readers may find difficult at first. Deep learning models are complex systems and understanding the behavior of even simple neural networks is a non-trivial exercise. Fortunately, PyTorch acts as a high-level framework around these complicated systems, so it is possible to achieve very good results without an expert understanding of the theoretical foundations.

Installing the software is easy, and essentially only two packages are required: the Anaconda distribution of Python, and PyTorch itself. The software runs on Windows 7 and 10 , macOS 10.10 or above, and most versions of Linux. It can be run on a desktop machine or in a server environment. All the code in this book was tested using PyTorch version 1.0 and Python 3, running on Ubuntu 16.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.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.packt.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-with-PyTorch-Quick-Start-Guide. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

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/9781789534092_ColorImages.pdf.

Conventions used

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: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

import numpy as np
x = np.array([[1,2,3],[4,5,6],[1,2,5]]) 
y = np.linalg.inv(x) 
print (y) 
print (np.dot(x,y))

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 numpy as np
x = np.array([[1,2,3],[4,5,6],[1,2,5]]) 
y = np.linalg.inv(x) 
print (y) 
print (np.dot(x,y))

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.