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.
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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.
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 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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Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code.
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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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“Deep Learning with PyTorch” by Eli Stevens, Luca Antiga, Thomas Viehmann: This book provides a hands-on approach to learning deep learning and PyTorch. It covers the basics of deep learning and PyTorch, and provides hands-on examples of implementing various deep learning models using PyTorch.
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Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation
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These books are not required for you to learn PyTorch, but they are highly recommended for you to deepen your knowledge.
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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This book enables you to solve the trickiest of problems in computer vision using deep learning algorithms and techniques. You will learn to use several different algorithms for different CV problems such as classification, detection, segmentation, and more using Pytorch.
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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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This book, written by an HPC expert with over 25 years of experience, guides you through enhancing model training performance using PyTorch. Here you'll learn how model complexity impacts training time and discover performance tuning levels to expedite the process, as well as utilize PyTorch features, specialized libraries, and efficient data pipelines to optimize training on CPUs and accelera…
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With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. This book starts by taking you through the core concepts necessary to understand how each component…
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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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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.
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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This list is curated by MentorCruise and can include Amazon affiliate links. Have any other suggestions? Add here.
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