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