Packt Publishing, 2024. — 746 p. Key Features: Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models. Build solutions for real-world computer vision problems using PyTorch. All the code files are available...
Independently published, 2021. — 1187 p. Version 1.0, 2021-05-18. If you're looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that's easy and enjoyable to read, this is it:-). The book covers the basics of gradient descent up to fine-tuning large NLP models (BERT and GPT-2) using...
Independently published, 2024. — 288 p. — ASIN: B07N7KP6NJ. PyTorch: A Comprehensive Guide to Deep Learning for Beginners – A Step-by-Step Guide is designed to demystify the world of deep learning, making it accessible to individuals with little to no programming experience. It focuses on practical implementation using PyTorch, a popular and user-friendly framework. Why This...
O’Reilly Media, Inc., 2020. — 624 p. — ISBN: 9781492045526. 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...
O’Reilly Media, Inc., 2020. — 178 p. — ISBN13: 978-1-492-04545-8. Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you’ll learn how to train a model to accomplish a wide range of tasks...
Amazon Digital Services LLC, 2019. — 120 p. his 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....
Packt, 2023. — 444 p. — Second Edition. 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 from your data and build complex neural network models. You'll create convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) and...
Apress, 2022. — 240 p. Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch. The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the...
Independently published, 2024. — 117 p. — ASIN: B0CSV4H1FD. Large Language Models (LLMs) are revolutionizing AI, understanding and generating human language like never before. But harnessing their full potential requires the right tools. This book takes you on a deep dive into PyTorch, the leading framework for building and optimizing LLMs. This book is your practical guide to...
Apress, 2019. — 184 p. — ISBN: 978-1484242575. Get up to speed with the deep learning concepts of Pytorch using 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...
2nd Edition. — Apress Media, LLC, 2022. — 290 p. — ISBN: 978-1-4842-8925-9. 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...
O’Reilly Media, Inc., 2021. — 310 p. — ISBN: 978-1-492-09000-7. 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....
O’Reilly, 2019. — 220 p. — ISBN: 1492045357. Deep learning is changing everything. This machine-learning method has already surpassed traditional computer vision techniques, and the same is happening with NLP. If you're looking to bring deep learning into your domain, this practical book will bring you up to speed on key concepts using Facebook's PyTorch framework. Once author...
O’Reilly Media, 2019. — 256 p. - ISBN: 1491978236 Natural Language Processing (NLP) offers unbounded opportunities for solving interesting problems in artificial intelligence, making it the latest frontier for developing intelligent, deep learning-based applications. If you’re a developer or researcher ready to dive deeper into this rapidly growing area of artificial...
GitforGits, October 5, 2023. — 238 p. — ISBN-13: 978-8119177967. Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters. The book simplifies neural...
Manning Publications, 2020. — 520 p. — ISBN: 978-1617295263. Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with...
BPB Publications, 2024. — 310 p. — ISBN: 978-93-55517-494. Your key to transformer-based NLP, vision, speech, and multimodalities. Key Features Transformer architecture for different modalities and multimodalities. Practical guidelines to build and fine-tune transformer models. Comprehensive code samples with detailed documentation. Description. This book covers transformer...
Independently published, 2020. — 179 p. — ASIN B0895YQYFC. Learn how to solve real-world problems with Deep Learning models (NLP, Computer Vision, and Time Series). Go from prototyping to deployment with PyTorch and Python! PyTorch is the best Deep Learning library there (currently), period! Doing ML with PyTorch feels like a superpower (of course, there are bad parts, too)....
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