All the chapters (except Chapter 1, Getting Started with Deep Learning Using PyTorch and Chapter 9, What Next) have associated Jupyter Notebooks in the book's GitHub repository. The imports required for the code to run may not be included in the text to save space. You should be able to run all of the code from the Notebooks.

The book focuses on practical illustrations, so run the Jupyter Notebooks as you read the chapters.

Access to a computer with a GPU will help run the code quickly. There are companies such as paperspace.com and www.crestle.com that abstract a lot of the complexity required to run deep learning algorithms.

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-with-PyTorch. 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!

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/DeepLearningwithPyTorch_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: "The custom class has to implement two main functions, namely __len__(self) and __getitem__(self, idx)."

A block of code is set as follows:

x,y = get_data() # x - represents training data,y -                 represents target variables

w,b = get_weights() # w,b - Learnable parameters

for i in range(500):
    y_pred = simple_network(x) # function which computes wx + b
    loss = loss_fn(y,y_pred) # calculates sum of the squared differences of y and y_pred
    
if i % 50 == 0: 
        print(loss)
    optimize(learning_rate) # Adjust w,b to minimize the loss

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

conda install pytorch torchvision cuda80 -c soumith

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