Top PyTorch books recommended by experts

At MentorCruise, we are all about making the most out of the experience of others. As part of that, we've connected and asked dozens of experts and professionals about their favourite PyTorch books – and here are the answers.

Table of Contents

Fundamentals of PyTorch

Understanding the concepts of PyTorch starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics.

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Make Your First GAN With PyTorch

A gentle introduction to Generative Adversarial Networks, and a practical step-by-step tutorial on making your own with PyTorch. This beginner-friendly guide will give you hands-on experience: learning PyTorch basics. developing your first PyTorch neural network.

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PyTorch Pocket Reference: Building and Deploying Deep Learning Models

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.

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Inside Deep Learning: Math, Algorithms, Models

It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you'll dive into math, theory, and practical applications.

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Deep Learning with PyTorch: Guide for Beginners and Intermediate

This book is an exploration of deep learning in Python using PyTorch. The author guides you on how to create neural network models using PyTorch in Python. You will know the initial steps of getting started with PyTorch in Python. This involves installing PyTorch and writing your first code. PyTorch works using the concept of graphs. The author helps you know how build neural network graphs in…

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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.
You’ll start with basic motivations behind causal thinking and a comprehensive introduction to …

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Additional PyTorch Reading

These books are not required for you to learn PyTorch, but they are highly recommended for you to deepen your knowledge.

Natural Language Processing with PyTorch

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If youâ??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.

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Deep Learning with PyTorch Quick Start Guide

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without …

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Pytorch Deep Learning By Example

Artificial Intelligence (AI), Machine Learning especially Deep Learning has made tremendous progress in recent years. It starts to spread to all industries. Unless you are a refresh graduated student with AI/deep learning major, many of us do not have a formal machine learning/deep learning training before, so it is time to keep updated with latest technology.

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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics.

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PyTorch Recipes: A Problem-Solution Approach

Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts.

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The Deep Learning with PyTorch Workshop: Build deep neural networks and artificial intelligence applications with PyTorch

Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch.

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Specializations and Deeper PyTorch Knowledge

You've got your basics in order – time to move on to some advanced and specialized concepts. PyTorch is evolving every day, these books can help you master it.

Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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,…

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