Jha Mastering PyTorch

Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond
2. Auflage 2024
ISBN: 978-1-80107-996-9
Verlag: De Gruyter
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 558 Seiten

No detailed description available for "Mastering PyTorch".

Autoren/Hrsg.

Jha, Ashish Ranjan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

Table of Contents - Overview of Deep Learning using PyTorch
- Deep CNN architectures
- Combining CNNs and LSTMs
- Deep Recurrent Model Architectures
- Advanced Hybrid Models
- Graph Neural Networks
- Music and Text Generation with PyTorch
- Neural Style Transfer
- Deep Convolutional GANs
- Image Generation Using Diffusion
- Deep Reinforcement Learning
- Model Training Optimizations
- Operationalizing PyTorch Models into Production
- PyTorch on Mobile Devices
- Rapid Prototyping with PyTorch
- PyTorch and AutoML
- PyTorch and Explainable AI
- Recommendation Systems with TorchRec
- PyTorch and Hugging Face

Leseproben

Preface

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

The book starts with a quick overview of PyTorch and explores convolutional neural network (CNN) architectures for image classification. Similarly, you will explore recurrent neural network (RNN) architectures as well as Transformers and use them for sentiment analysis. Next, you will learn how to create arbitrary neural network architectures and build Graph neural networks (GNNs). As you advance, you’ll apply deep learning (DL) across different domains such as music, text, and image generation using generative models including Generative adversarial networks (GANs) and diffusion.

Next, you’ll build and train your own deep reinforcement learning models in PyTorch, as well as interpreting DL models. You will not only learn how to build models but also how to deploy them into production and to mobile devices (Android and iOS) using expert tips and techniques. Next, you will master the skills of training large models efficiently in a distributed fashion, searching neural architectures effectively with AutoML, as well as rapidly prototyping models using fastai. You’ll then create a recommendation system using PyTorch. Finally, you’ll use major Hugging Face libraries together with PyTorch to build cutting edge artificial intelligence (AI) models.

By the end of this PyTorch book, you’ll be well equipped to perform complex deep learning tasks using PyTorch to build smart AI models.

Who this book is for

This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 2.x. Working knowledge of deep learning with Python programming is required.

What this book covers

, , includes brief notes on various deep learning terminologies and concepts useful for understanding later parts of this book. This chapter also gives a quick overview of PyTorch in contrast with TensorFlow as a language and tools that will be used throughout this book for building deep learning models. Finally, we train a neural network model using PyTorch.

, , is a rundown of the most advanced deep CNN model architectures that have been developed in recent years. We use PyTorch to create many of these models and train them for appropriate tasks.

, , walks through an example where we build a neural network model with a CNN and LSTM that generates text/captions as output when given images as inputs using PyTorch.

, , goes through recent advancements in recurrent neural architectures, specifically RNNs, LSTMs, and GRUs. Upon completion, you will be able to create complex recurrent architecture in PyTorch.

, , discusses some advanced, unique hybrid neural architectures such as the Transformers that have revolutionized the world of natural language processing. This chapter also discusses RandWireNNs, taking a peek into the world of neural architecture search, using PyTorch.

, , walks us through the basic concepts behind GNNs, different kinds of graph learning tasks, and different types of GNN model architectures. The chapter then dives deep into a few of those architectures, namely Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). This chapter uses PyTorch Geometric as the library of choice for building GNNs in PyTorch.

, , demonstrates the use of PyTorch to create deep learning models that can compose music and write text with practically nothing being provided to them at runtime.

, , discusses a special type of CNN model that can mix multiple input images and generate artistic-looking arbitrary images.

, , explains GANs and trains one using PyTorch on a specific task.

, , implements a diffusion model from scratch as a state-of-the-art text-to-image generation model, using PyTorch.

, , explores how PyTorch can be used to train agents on a deep reinforcement learning task, such as a player in a video game.

, , explores how to efficiently train large models with limited resources through distributed training as well as mixed precision training practices in PyTorch. By the end of this chapter, you will have mastered the skill of training large models efficiently using PyTorch.

, , runs through the process of deploying a deep learning model written in PyTorch into a real production system using Flask and Docker, as well as TorchServe. Then you’ll learn how to export PyTorch models both using TorchScript and ONNX. You’ll also learn how to ship PyTorch code as a C++ application. Finally, you’ll learn how to use PyTorch on some of the popular cloud computing platforms.

, , walks through the process of using various pre-trained PyTorch models and deploying them on different mobile operating systems – Android and iOS.

, , discusses various tools and libraries such as fastai and PyTorch Lightning that make the process of model training in PyTorch several times faster. This chapter also explains how to profile PyTorch code to understand resource utilization.

, , walks through setting up ML experiments effectively using AutoML and Optuna with PyTorch.

, , focuses on making machine learning models interpretable to a layman using tools such as Captum, combined with PyTorch.

, , builds a deep-learning-based movie recommendation system from scratch using PyTorch.

, , discusses how to use Hugging Face libraries such as Transformers, Accelerate, Optimum, and so on, with PyTorch to build cutting-edge multi-modal AI models.

To get the most out of this book

To fully benefit from this book, it is necessary that you meet the following prerequisites and recommendations:

• Hands-on Python experience as well as basic knowledge of PyTorch is expected. Because most exercises in this book are in the form of notebooks, a working experience with Jupyter notebooks is expected.
• Some of the exercises in some of the chapters might require a GPU for faster model training, and therefore having an NVIDIA GPU is a plus.
• Finally, having registered accounts with cloud computing platforms such as AWS, Google Cloud, and Microsoft Azure will be helpful to navigate parts of as well as to facilitate distributed training in over several virtual machines.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/arj7192/MasteringPyTorchV2. 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://packt.link/gbp/9781801074308.

Conventions used

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

: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Mount the downloaded disk image file as another disk in your system.”

A block of code is set as follows:

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

Any command-line input or output is...

Über Autor(innen)

Jha Ashish Ranjan:

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.