{"product_id":"hands-on-generative-ai-with-transformers-husn-ara-9798265756282","title":"Hands-on Generative AI with Transformers and Diffusion Models","description":"\u003cb\u003eHands-on Generative AI with Transformers and Diffusion Models \u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eThis book is a comprehensive guide for anyone who wants to understand, build, and deploy generative AI systems in real-world contexts. Written for engineers, data scientists, and AI practitioners, this book takes you from the \u003cb\u003ecore concepts of generative modeling\u003c\/b\u003e all the way to advanced architectures and enterprise applications. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eKey Highlights: \u003c\/b\u003e\u003col\u003e\n\u003cli\u003eThe foundations chapter introduces \u003cb\u003etokens, embeddings, and latent space\u003c\/b\u003e, the building blocks of generative AI.\u003c\/li\u003e\n\u003cli\u003eThe \u003cb\u003etransformer and attention mechanism\u003c\/b\u003e chapter explains the core architecture that enabled LLMs and multimodal systems.\u003c\/li\u003e\n\u003cli\u003eThe section on \u003cb\u003ediffusion models\u003c\/b\u003e covers the noise-to-data process behind breakthroughs like Stable Diffusion and text-to-video.\u003c\/li\u003e\n\u003cli\u003eIn industry use cases, \u003cb\u003edrug discovery and healthcare AI\u003c\/b\u003e show how generative models drive molecule design and protein analysis.\u003c\/li\u003e\n\u003cli\u003eThe book closes with \u003cb\u003eemerging research trends\u003c\/b\u003e, giving engineers a roadmap into multimodality, reasoning agents, and alignment challenges.\u003c\/li\u003e\n\u003c\/ol\u003e\u003cbr\u003eThe book begins with the \u003cb\u003efoundations of generative AI\u003c\/b\u003e, introducing tokens, embeddings, and latent spaces, along with essential mathematical tools like probability, distributions, and optimization. From there, readers move into \u003cb\u003eneural network fundamentals\u003c\/b\u003e before diving into the architectures that revolutionized AI - \u003cb\u003eautoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), transformers, and diffusion models\u003c\/b\u003e. Each architecture is explained in detail with \u003cb\u003ePython code examples\u003c\/b\u003e, giving readers hands-on experience with implementation. \u003cp\u003e\u003c\/p\u003eIn the middle sections, the book covers \u003cb\u003elarge language models (LLMs)\u003c\/b\u003e, including pretraining, fine-tuning, tokenization, and alignment strategies such as reinforcement learning with human feedback (RLHF). It also explores \u003cb\u003emultimodal generative AI\u003c\/b\u003e, where text, images, audio, and video converge in state-of-the-art models like CLIP, Flamingo, Gemini, and GPT-4. The \u003cb\u003escaling laws and infrastructure chapter\u003c\/b\u003e guides readers through efficient training, GPU\/TPU clusters, model parallelism, and cost optimization, making this book highly practical for engineers building at scale. \u003cp\u003e\u003c\/p\u003ePart IV moves into \u003cb\u003eindustry applications and case studies\u003c\/b\u003e, showcasing how generative AI powers chatbots, virtual assistants, content generation, AI art and design, code generation (e.g., GitHub Copilot), and \u003cb\u003esynthetic data for computer vision and medical imaging\u003c\/b\u003e. It also demonstrates groundbreaking use cases in \u003cb\u003edrug discovery, healthcare AI, finance, and risk modeling\u003c\/b\u003e, showing how generative models are accelerating innovation across industries. \u003cp\u003e\u003c\/p\u003eThe final part of the book addresses \u003cb\u003eethics, safety, and the future of generative AI\u003c\/b\u003e. Topics include bias in generative systems, responsible use, AI governance, and adversarial risks like deepfakes and model jailbreaking. Engineers will also gain insight into \u003cb\u003eemerging research trends\u003c\/b\u003e such as multimodal agents, reasoning systems, continual learning, privacy-preserving AI, and the debate between \u003cb\u003eopen-source vs. proprietary models\u003c\/b\u003e. Perspectives on \u003cb\u003eArtificial General Intelligence (AGI)\u003c\/b\u003e and alignment research round out the book, giving readers a forward-looking roadmap. \u003cp\u003e\u003c\/p\u003ePacked with \u003cb\u003eclear explanations, detailed diagrams (described in-text), and runnable Python code\u003c\/b\u003e, this book equips generative AI engineers with both the theoretical foundations and the practical skills to innovate responsibly. Whether you're building \u003cb\u003eLLMs, diffusion models, multimodal systems, or enterprise-grade generative AI applications\u003c\/b\u003e, this guide will accelerate your journey.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Husn Ara\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798265756282\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/16\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 346\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.02lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.72d","brand":"Husn Ara","offers":[{"title":"Paperback","offer_id":47965485334783,"sku":"9798265756282","price":30.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_b92ccf9e-66ca-4176-ad71-9ccfd420dd0a.jpg?v=1767280887","url":"https:\/\/www.whiterainbookhouse.com\/products\/hands-on-generative-ai-with-transformers-husn-ara-9798265756282","provider":"WR Book House","version":"1.0","type":"link"}