8 New Generative Models Books Reshaping AI in 2025

Jonathan Dinu brings a focused lens to the evolving landscape of generative AI with his detailed exploration of large multimodal models using PyTorch and Hugging Face. This book guides you through the practical aspects of programming these sophisticated systems, emphasizing the integration of diverse data types to enhance AI capabilities. Readers will gain hands-on insights into model architecture, training techniques, and deployment strategies tailored for multimodal AI applications. If you're involved in AI development or research and want to deepen your technical expertise on the latest generative frameworks, this book offers a precise and technical roadmap without unnecessary jargon.

The latest edition by Joseph Babcock and Raghav Bali ventures deeply into the evolving landscape of generative AI, specifically through Python and PyTorch frameworks. You gain hands-on experience building projects with advanced models like GANs, VAEs, and LLMs, including GPT-4 and open-source variants like Llama. The book walks you through optimizing diffusion models and crafting prompt engineering strategies, equipping you to tackle diverse tasks from image generation to natural language processing. While it demands some coding and math familiarity, the clear chapter on LLM optimization and practical toolboxes make it especially useful for developers ready to advance their AI skills.

This personalized book delves into the latest breakthroughs and techniques shaping generative models in 2025. It explores emerging architectures, training methods, and novel applications tailored specifically to your interests and background. By focusing on your unique goals, the content reveals cutting-edge developments such as multimodal integration, advanced diffusion models, and innovative transformer variants that are redefining AI capabilities. This tailored exploration not only keeps you updated with recent discoveries but also examines how these advancements interact and evolve, providing a deep understanding of the current AI generative landscape.

What happens when deep engineering expertise meets cutting-edge generative AI? Omar Sanseviero and his co-authors bring you a hands-on guide rooted in their real-world experience at Hugging Face and Google. You get to learn not just the theory behind transformers and diffusion models but also practical skills like fine-tuning models, customizing them for unique text and image generation, and combining building blocks for creative applications. Chapters include detailed code walkthroughs and examples that help you grasp complex concepts by doing. This book suits ambitious practitioners who want to build and adapt generative AI models beyond off-the-shelf solutions.

Drawing from deep expertise in Java development and AI, Banu Parasuraman crafted this guide to address the growing need for integrating large language models and Generative AI within the Spring Framework. You’ll learn how to practically combine AI technologies with Java, starting from foundational concepts and advancing to sophisticated implementations, including handling security and ethical concerns. The book walks you through performance optimization and scalability strategies for AI-driven applications, making it a solid choice if you’re an intermediate to advanced Java developer eager to expand your skillset into AI. While prior AI knowledge helps, the book builds your understanding methodically, focusing on real coding applications rather than abstract theory.

Dr. Mark Liu brings over two decades of coding experience and a strong academic foundation in finance to this book, crafting an accessible path into the world of generative AI using PyTorch. You'll find yourself building a variety of models, from simple English-to-French translators to complex diffusion models producing realistic images, all while learning key concepts like GANs, transformers, and large language models. The book's hands-on approach demystifies advanced topics like LangChain integration and zero-shot agents without requiring prior PyTorch knowledge. If you have a basic grasp of Python and machine learning fundamentals, this book equips you with the skills to create and experiment with generative AI models effectively.

This tailored book explores the evolving landscape of generative AI technologies, focusing on the latest trends and discoveries expected to shape 2025 and beyond. It examines emerging generative models, their applications, and the underlying principles driving innovation. The content is carefully matched to your background and interests, providing a personalized journey through recent advancements and future possibilities. By concentrating on the topics you find most relevant, this book reveals cutting-edge insights and helps you stay well-informed about groundbreaking developments. This tailored approach ensures you gain a deep understanding of new generative AI methods, empowering you to navigate and anticipate rapid changes in the field with confidence.

Drawing from nearly a decade leading generative AI projects at Microsoft, Amit Bahree offers a grounded exploration of how AI models can reshape business workflows. You’ll learn concrete techniques like prompt engineering, retrieval-augmented generation (RAG), and multi-modal integration, with detailed guidance on deploying AI at enterprise scale. The book also tackles real challenges such as hallucinations and security risks, presenting architectural patterns and strategies to safely embed generative AI in your products. If you’re an enterprise architect or senior developer aiming to modernize your tech stack with AI, Bahree’s experience-based approach delivers practical insights without hype.

Jonathan M. Sterling brings his computer science expertise to demystify generative AI, guiding you from foundational concepts to advanced models like GANs and autoregressive networks. This book walks you through practical applications across industries such as healthcare and finance, while also equipping you with hands-on experience using frameworks like TensorFlow and PyTorch. You’ll find detailed chapters on leveraging large language models including ChatGPT, plus extensive prompt collections to spark creativity. If you're eager to build real skills in AI and understand its latest innovations, this book lays out a clear, approachable path that balances theory with practice.

The latest insights in "Generative AI Coding: Techniques and Applications" come from Anand Vemula, whose approach grounds you firmly in the foundational and emerging aspects of generative AI. You’ll learn the nuts and bolts of key architectures like VAEs, GANs, and transformer models, alongside practical coding exercises using TensorFlow and PyTorch. The book’s chapters on model optimization and deployment in production environments offer concrete skills for machine learning engineers aiming to bridge theory and real-world application. Whether you’re a data scientist or an AI enthusiast, this book equips you to grasp how generative AI reshapes industries from healthcare to entertainment with clear, hands-on examples.