Table of Contents
Best PyTorch Books 2023
PyTorch Pocket Reference: Building and Deploying Deep Learning Models
- Papa, Joe (Author)
- English (Publication Language)
- 307 Pages - 06/15/2021 (Publication Date) - O'Reilly Media (Publisher)
This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers.
Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices.
Learn basic PyTorch syntax and design patterns
Create custom models and data transforms
Train and deploy models using a GPU and TPU
Train and test a deep learning classifier
Accelerate training using optimization and distributed training
Access useful PyTorch libraries and the PyTorch ecosystem
This is one of the best PyTorch books in 2023.
Deep Learning with PyTorch: Build, train, and tune neural networks using Python tool
- Stevens, Eli (Author)
- English (Publication Language)
- 520 Pages - 08/04/2020 (Publication Date) - Manning (Publisher)
by Eli Stevens, Luca Antiga and Thomas Viehmann teaches you how to create deep learning systems and neural networks with PyTorch. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise.
Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. You will:
Understand deep learning data structures such as tensors and neural networks.
Best Practices for PyTorch Tensor API, Loading Data into Python, and Displaying Results
PyTorch Implementation of loss modules and functions
Using previously trained PyTorch Hub models
Networking methods with limited inputs.
Examine unreliable results to diagnose and troubleshoot your neural network
Improve your results with increased data, better Pytorch model architecture, and fine tuning
Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
- Pointer, Ian (Author)
- English (Publication Language)
- 217 Pages - 10/29/2019 (Publication Date) - O'Reilly Media (Publisher)
Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks.
Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.
Learn how to deploy deep learning models to production
Explore PyTorch use cases from several leading companies
Learn how to apply transfer learning to images
Apply cutting-edge NLP techniques using a model trained on Wikipedia
Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model
Debug PyTorch models using TensorBoard and flame graphs
Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud
This is one of the best PyTorch books for Deep learning in 2023.
Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
- Howard, Jeremy (Author)
- English (Publication Language)
- 621 Pages - 08/25/2020 (Publication Date) - O'Reilly Media (Publisher)
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.
Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.
Train models in computer vision, natural language processing, tabular data, and collaborative filtering
Learn the latest deep learning techniques that matter most in practice
Improve accuracy, speed, and reliability by understanding how deep learning models work
Discover how to turn your models into web applications
Implement deep learning algorithms from scratch
Consider the ethical implications of your work
Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
- Rao, Delip (Author)
- English (Publication Language)
- 254 Pages - 02/19/2019 (Publication Date) - O'Reilly Media (Publisher)
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.
Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.
Explore computational graphs and the supervised learning paradigm
Master the basics of the PyTorch optimized tensor manipulation library
Get an overview of traditional NLP concepts and methods
Learn the basic ideas involved in building neural networks
Use embeddings to represent words, sentences, documents, and other features
Explore sequence prediction and generate sequence-to-sequence models
Learn design patterns for building production NLP systems
Mastering PyTorch: Build powerful deep learning architectures using advanced PyTorch features, 2nd Edition
- Amazon Kindle Edition
- Jha, Ashish Ranjan (Author)
- English (Publication Language)
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.
The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You’ll then work with recurrent neural network (RNN) architectures 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 and explore the world of generative adversarial networks (GANs). You’ll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you’ll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.
By the end of this PyTorch 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 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
Easily interpret machine learning (ML) models written in PyTorch using Captum
Design ResNets, LSTMs, Transformers, and more using PyTorch
Find out how to use PyTorch for distributed training using the torch.distributed API
PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
- Mishra, Pradeepta (Author)
- English (Publication Language)
- 292 Pages - 12/08/2022 (Publication Date) - Apress (Publisher)
Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.
You’ll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you’ll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch.
This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail.
Each chapter includes recipe code snippets to perform specific activities. By the end of this book, you will be able to confidently build neural network models using PyTorch.
What You Will Learn Utilize new code snippets and models to train machine learning models using PyTorchTrain deep learning models with fewer and smarter implementationsExplore the PyTorch framework for model explainability and to bring transparency to model interpretationBuild, train, and deploy neural network models designed to scale with PyTorchUnderstand best practices for evaluating and fine-tuning models using PyTorchUse advanced torch features in training deep neural networksExplore various neural network models using PyTorchDiscover functions compatible with sci-kit learn compatible modelsPerform distributed PyTorch training and execution
PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python
- Amazon Kindle Edition
- Liu, Yuxi (Hayden) (Author)
- English (Publication Language)
by Yuxi (Hayden) Liu will explore important RL concepts and algorithm implementation in PyTorch 1.x. With this book, you’ll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning.
You’ll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You’ll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you’ll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game.
You will:
Use Q-learning and the SARSA (state – action – reward – state – action) algorithm to solve various Gridworld problems
Develop a multi-armed bandit algorithm to optimize display advertising
Improve learning and control processes using Deep Q-Networks
Simulate Markov decision-making processes, OpenAI Gym environments, and other common control problems
Select and build RL models, evaluate their performance, optimize and implement them
Use Policy Gradient Methods to Solve Continuous RL Problems