Packt Publishing, 2018. — 262 p. — ISBN: 1788624335. 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...
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, 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...
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...
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...
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...
СПб.: Питер, 2020. — 256 с. — (Бестселлеры O’Reilly). — ISBN: 978-5-4461-1241-8. Обработка текстов на естественном языке (Natural Language Processing, NLP) - крайне важная задача в области искусственного интеллекта. Успешная реализация делает возможными такие продукты, как Alexa от Amazon и Google Translate. Эта книга поможет вам изучить PyTorch - библиотеку глубокого обучения...
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 from the basics of gradient descent all the way up to fine-tuning large NLP models (BERT and GPT-2)...
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. Research...
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...
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...
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...
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) is, period! Doing ML with PyTorch feels like a superpower (of course, there are bad parts, too)....
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 on...
Amazon Digital Services LLC, 2019. — 120 р. 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....
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...
Manning Publications, 2024. — 298 p. Изучите генеративный искусственный интеллект с помощью PyTorch (MEAP v2) Create your own generative AI models for text, images, music, and more! Generative AI tools like ChatGPT, Bard, and DALL-E have transformed the way we work. Learn Generative AI with PyTorch takes you on an incredible hands-on journey through creating and training AI...
Apress, 2019. — 184 р. — 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...
Packt Publishing, 2020. — 647 p. — ISBN 1839213477, 9781839213472. Packed with hands-on implementations of deep learning techniques to build image processing applications using PyTorch. Each chapter is accompanied by a GitHub folder with code notebooks and questions to cement your understanding. Key Features - Implement solutions to 50 real-world computer vision applications...
Leanpub, 2022. — 1042 p. 2022-02-12 (v1.1.1) 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 :-) PyTorch is the fastest-growing framework for developing deep learning models and it has a huge ecosystem. That is, there are many tools...
Packt Publishing, 2021. — 450 p. — ISBN 9781789614381. Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer...
2nd Edition. — GitforGits, 2024. — 314 p. — ISBN-13 978-8119177916. "Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent...
Packt, 2019. — 250 p. — ISBN: 978-1788834131. Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep learning models from prototyping to production Book...
BPB Publications, 2024. — 310 р. — 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...
Packt Publishing Ltd., 2020. — 448 p. —ISBN: 978-1-78953-051-3. Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models 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...
Packt, 2019. — 340 p. — ISBN: 9781838551964. Learn Use Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problems Develop a multi-armed bandit algorithm to optimize display advertising Scale up learning and control processes using Deep Q-Networks Simulate Markov Decision Processes, OpenAI Gym environments, and other common control...
Packt, 2019. — 254 p. — ISBN: 978-1789804591. Implement techniques such as image classification and natural language processing (NLP) by understanding the different neural network architectures Key Features Understand deep learning and how it can solve complex real-world problems Apply deep learning for image classification and text processing using neural networks Develop deep...
Independently published, 2024. — 540 р. — ISBN 979-8329915327, ASIN B0D8JKJR9Y. Embark on an enlightening journey through the world of machine learning and artificial intelligence with our comprehensive guide to PyTorch. As one of the premier frameworks in the field, PyTorch has rapidly gained traction among researchers, developers, and enthusiasts alike, owing to its intuitive...
Packt Publishing, 2020. — 452 p. — ISBN: 978-1-83864-483-3. Discover powerful ways to explore deep learning algorithms and solve real-world computer vision problems using Python Developers can gain a high-level understanding of digital images and videos using computer vision techniques. With this book, you’ll learn how to solve the trickiest of problems in computer vision (CV)...
Packt Publishing Ltd., 2020. — 276 p. — ISBN: 978-1-78980-274-0. Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and unstructured data In the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a...
2nd edition. — Packt Publishing Ltd., 2019. — 304 p. — ISBN: 978-1-83855-300-5. Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.x. Key Features Implement deep learning techniques to build neural network architectures using PyTorch 1.x; Understand GPU computing to perform heavy deep learning...
GitforGits, 2023. — 321 p. — ISBN-13 978-8196288372. This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for Deep Learning applications. It starts with an introduction to PyTorch, its various advantages over other Deep Learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch – tensors, learning their...
Packt Publishing, 2020. — 338 p. — ISBN: 978-1-83855-704-1. Use PyTorch to build end-to-end artificial intelligence systems using Python Artificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, you’ll get to grips with building deep learning apps, and how you can use PyTorch for...
Packt Publishing, 2021. — 340 p. — ISBN 978-1-80020-810-0. Harness the power of the easy-to-use, high-performance fastai framework to rapidly create complete deep learning solutions with few lines of code Key Features Discover how to apply state-of-the-art deep learning techniques to real-world problems Build and train neural networks using the power and flexibility of the...
2nd Edition. — Packt Publishing Ltd., 2020. — 330 p. — ISBN: 978-1-83898-921-7. 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...
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