8 New PyTorch Books Reshaping AI Development in 2025

This book challenges the conventional approach to learning generative AI by focusing on current, practical projects that use Python and PyTorch to implement cutting-edge models like GANs and large language models. You’ll explore detailed techniques including PEFT and LoRA for efficient model fine-tuning, and delve into retrieval augmented generation methods using tools like LangChain and LlamaIndex. The chapters on diffusion models and style transfer offer hands-on experience in image generation, while sections on prompt engineering and optimization deepen your grasp of NLP advancements. This book suits data scientists and developers ready to build advanced AI applications with a solid understanding of Python and statistics.

When Dr. Mark Liu, a finance professor with over two decades of coding experience, discovered the surge of generative AI technologies like ChatGPT and DALL-E, he crafted this book to bridge the gap between theory and hands-on application using PyTorch. You’ll explore how to build and train models such as GANs, Transformers, and diffusion models, progressing from simple image generation to complex large language models. Chapters guide you through projects like creating a Hemingway-style text generator and a music generator inspired by Mozart, all using Python fundamentals you likely already know. This book suits Python programmers eager to deepen their machine learning skills by developing practical generative AI models themselves.

This tailored book explores the latest PyTorch advancements emerging in 2025, focusing on discoveries and innovations that align with your background and goals. It covers cutting-edge features like torch.compile() and multimodal modeling, examining how these changes are reshaping AI development today. By concentrating on your specific interests, this personalized guide reveals new techniques and research findings that keep you ahead in the fast-evolving PyTorch ecosystem. Whether you're deepening expertise or expanding into new subfields, this book offers a customized exploration crafted to match your unique learning path and aspirations.

Drawing from his deep expertise in PyTorch and CUDA, Matthew Rosch offers a focused guide that cuts through foundational theory to prioritize hands-on skills with the latest PyTorch 2.3 and CUDA 12 features. You’ll explore practical model-building techniques, from feedforward networks to transformers, and dive into performance enhancements like torch.compile() and quantization. The book’s use of a fish dataset project grounds these advanced topics, making complex concepts accessible for those already familiar with neural networks but eager to update their toolkit. If you're aiming to efficiently deploy models with tools like TorchServe or migrate TensorFlow models via ONNX, this book delivers clear, concise guidance without unnecessary detours.

Unlike most PyTorch books that focus solely on foundational concepts, Marija Jegorova's text zeroes in on Generative Adversarial Networks (GANs) with a hands-on approach that takes you through building and optimizing GAN architectures using PyTorch 2.x. You’ll gain practical skills in crafting models that generate images, text, audio, and even 3D models, with chapters dedicated to popular frameworks like StyleGAN and CycleGAN. The book also explores emerging hybrid models combining GANs with Transformers and Diffusion Models, giving you insight into the cutting edge of generative AI. If you're comfortable with Python and want to deepen your expertise in generative models for real-world applications, this book offers clear, example-driven guidance without unnecessary theory overload.

Mikhail Agladze challenges the conventional wisdom that mastering PyTorch requires a steep learning curve by laying out the framework's core principles with clarity and precision. You’ll gain hands-on experience with tensor operations, automatic differentiation, and neural network construction, all explained through detailed examples that bridge theory and practice. The book walks you through optimization techniques and showcases applications in computer vision, natural language processing, and reinforcement learning, making it a practical guide for both newcomers and experienced practitioners. If you’re aiming to deepen your understanding of PyTorch’s mechanics and leverage it for diverse AI projects, this text offers a solid foundation without unnecessary complexity.

This tailored AI-created book explores upcoming PyTorch features and innovations set to reshape AI development in 2025. It covers the latest advancements, such as torch.compile() improvements and multimodal model support, in ways that align with your unique background and interests. By focusing on emerging trends and discoveries tailored specifically to your goals, the book reveals how you can anticipate and leverage new PyTorch capabilities effectively. Through a personalized exploration, it examines cutting-edge techniques and evolving applications, providing insights that match your current expertise and desired learning outcomes. This approach allows you to stay ahead in the dynamic PyTorch ecosystem with content crafted just for you.

What happens when a seasoned AI leader with deep industry roots tackles computer vision? Kishore Ayyadevara and Yeshwanth Reddy developed this updated edition to bridge foundational deep learning concepts with the latest breakthroughs in vision and generative AI. You'll get hands-on guidance implementing architectures like transformers, GANs, and diffusion models using PyTorch, complete with practical examples such as facial keypoint recognition and zero-shot segmentation. The book also addresses moving models into production, making it a solid pick if you're aiming to translate theory into deployable solutions. If you want more than just theory and prefer a roadmap blending fundamentals with cutting-edge applications, this book offers that balance.

When Anand Vemula noticed how rapidly large language models (LLMs) were reshaping AI, he set out to create a practical guide that demystifies their complexity using PyTorch. You’ll gain concrete skills in building, training, and fine-tuning LLMs, with detailed explanations of architectures like GPT and BERT alongside annotated code examples. The book walks you through crucial steps like data preparation and advanced training techniques, making it suitable if you want to move beyond theory into hands-on mastery. If you’re aiming to implement LLMs for applications such as chatbots or text generation, this concise 75-page guide offers focused insights without overwhelming you.

Ashish Ranjan Jha's extensive academic and industry background drives this deep dive into PyTorch's capabilities across diverse neural network architectures. You learn to build convolutional neural networks for image tasks, recurrent models for language, and cutting-edge transformer models, including integration with Hugging Face. The book also covers optimization techniques like multi-GPU training and deployment on mobile devices, making it practical for applied machine learning engineers. If you're aiming to transition from TensorFlow or deepen your PyTorch expertise with advanced, real-world examples, this book offers detailed insights without fluff.