{"product_id":"deep-learning-with-pytorch-second-edition","title":"Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models by Luca Antiga, Eli Stevens, Howard Huang and Thomas Viehmann","description":"\u003ch2\u003eDeep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models by Luca Antiga, Eli Stevens, Howard Huang and Thomas Viehmann\u003c\/h2\u003e\n\u003cp\u003eThe central thesis of the second edition remains unchanged but profoundly modernized: deep learning is fundamentally an engineering discipline of data tensor manipulation, gradient tracking, and numerical optimization. While many developer books treat neural networks as opaque, \"black-box\" abstractions where you simply change string parameters, \u003ci data-path-to-node=\"6\" data-index-in-node=\"347\"\u003eDeep Learning with PyTorch\u003c\/i\u003e forces the programmer to look under the hood. \u003cspan class=\"citation-73 citation-end-73\"\u003eIt teaches you exactly how data structures are stored in GPU memory, how automatic differentiation functions, and how code scales out to multi-node training clusters.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"citation-73 citation-end-73\"\u003e\u003cspan class=\"citation-72 citation-end-72\"\u003eHoward Huang preserves the original book's brilliant project-driven backbone: a multi-chapter, hands-on journey constructing a real-world, clinical-grade medical image classifier to detect lung tumors from raw CT scans.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-71 citation-end-71\"\u003eHowever, the Second Edition injects massive upgrades to address the generative AI revolution.\u003csup class=\"superscript\" data-turn-source-index=\"5\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-70\"\u003eHuang introduces entirely new sections on the structural building blocks of the \u003c\/span\u003e\u003cb data-path-to-node=\"7\" data-index-in-node=\"394\"\u003e\u003cspan class=\"citation-70\"\u003eTransformer architecture\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-70 citation-end-70\"\u003e, walking through tokenization, multi-head attention mechanisms, and context windows.\u003csup class=\"superscript\" data-turn-source-index=\"6\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-69 citation-end-69\"\u003eReaders don't just call pretrained models; they build text-generation pipelines and image diffusion models step-by-step using pure PyTorch syntax.\u003csup class=\"superscript\" data-turn-source-index=\"7\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"29\"\u003eAs our local technology sector races to build in-house AI capabilities, automated fintech pipelines, and digital health diagnostic platforms, the demand for true machine learning expertise has skyrocketed. However, many local software engineers and data science graduates find themselves hitting a technical ceiling. Relying on simple tutorials or fragile third-party wrappers, developers struggle to build production-grade systems that can converge on complex data or scale efficiently within limited GPU budgets.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eThe \u003cb data-path-to-node=\"30\" data-index-in-node=\"4\"\u003eSecond Edition\u003c\/b\u003e of \u003ci data-path-to-node=\"30\" data-index-in-node=\"22\"\u003eDeep Learning with PyTorch\u003c\/i\u003e delivers the exact, rigorous medicine our advanced engineering ecosystem needs. By combining the deep systems background of the original authors with Howard Huang's elite insights as a core PyTorch library developer, this book sets a new standard for technical education. It transforms developers from basic code-consumers into master AI architects capable of building enterprise-grade, self-correcting neural pipelines. It is an absolute must-read blueprint for any local technical leader, cloud engineer, or machine learning researcher determined to dominate modern AI infrastructure and ship highly optimized, scalable intelligent software.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Artificial Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong style=\"font-size: 0.875rem;\"\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47228046704825,"sku":null,"price":390.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Deep_Learning_with_PyTorch_Second_Edition.jpg?v=1779270853","url":"https:\/\/royalbooksbd.com\/products\/deep-learning-with-pytorch-second-edition","provider":"Royal Books BD","version":"1.0","type":"link"}