Mastering PyTorch

Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples

Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks

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Key Features

• Understand how to use PyTorch to build advanced neural network models
• Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker
• Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

Book Description

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models.

You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.

By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

What you will learn

• Implement text, vision, and music generation models using PyTorch
• Build a deep Q-network (DQN) model in PyTorch
• Deploy PyTorch models on mobile devices (Android and iOS)
• Become well versed in rapid prototyping using PyTorch with fastai
• Perform neural architecture search effectively using AutoML
• Easily interpret machine learning models using Captum
• Design ResNets, LSTMs, and graph neural networks (GNNs)
• Create language and vision transformer models using Hugging Face

Who this book is for

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

Table of Contents

1. Overview of Deep Learning using PyTorch
2. Deep CNN architectures
3. Combining CNNs and LSTMs
4. Deep Recurrent Model Architectures
5. Advanced Hybrid Models
6. Graph Neural Networks
7. Music and Text Generation with PyTorch
8. Neural Style Transfer
9. Deep Convolutional GANs
10. Image Generation Using Diffusion
11. Deep Reinforcement Learning
12. Model Training Optimizations
13. Operationalizing PyTorch Models into Production
14. PyTorch on Mobile Devices
15. Rapid Prototyping with PyTorch
16. PyTorch and AutoML
17. PyTorch and Explainable AI
18. Recommendation Systems with TorchRec
19. PyTorch and Hugging Face

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.