This is the code repository for Mastering PyTorch, published by Packt.
Build powerful neural network architectures using advanced PyTorch 1.x features
Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
This book covers the following exciting features:
- Implement text and music generating models using PyTorch
- Build a deep Q-network (DQN) model in PyTorch
- Export universal PyTorch models using Open Neural Network Exchange (ONNX)
- Become well-versed with rapid prototyping using PyTorch with fast.ai
- Perform neural architecture search effectively using AutoML
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
# define the optimization schedule for both G and D
opt_gen = torch.optim.Adam(gen.parameters(), lr=lrate)
opt_disc = torch.optim.Adam(disc.parameters(), lr=lrate)
Following is what you need for this book: This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Jupyter Notebook | Windows, Mac OS X, and Linux (Any) |
| 2 | Prebrably an NVIDIA GPU, but this is not mandatory | Windows, Mac OS X, and Linux (Any) |
| 3 | Python and PyTorch | Windows, Mac OS X, and Linux (Any) |
| 4 | AWS, Google cloud Platform and Azure account | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Ashish Ranjan Jha received his bachelor’s degree in electrical engineering from IIT Roorkee (India), his master’s degree in computer science from EPFL (Switzerland), and an MBA degree from the Quantic School of Business (Washington). He received distinctions in all of his degrees. He has worked for a variety of tech companies, including Oracle and Sony, and tech start-ups, such as Revolut, as a machine learning engineer. Aside from his years of work experience, Ashish is a freelance ML consultant, an author, and a blogger (datashines). He has worked on products/projects ranging from using sensor data for predicting vehicle types to detecting fraud in insurance claims. In his spare time, Ashish works on open source ML projects and is active on StackOverflow and kaggle (arj7192).