{"product_id":"hands-on-large-language-models","title":"Hands-On Large Language Models: Language Understanding and Generation by Jay Alammar, Maarten Grootendorst","description":"\u003ch2\u003eHands-On Large Language Models: Language Understanding and Generation by Jay Alammar, Maarten Grootendorst\u003c\/h2\u003e\n\u003cp data-path-to-node=\"7\"\u003eThe core philosophy of \u003ci data-path-to-node=\"7\" data-index-in-node=\"23\"\u003eHands-On Large Language Models\u003c\/i\u003e is that \u003cb data-path-to-node=\"7\" data-index-in-node=\"62\"\u003etrue mastery of generative AI requires a balanced understanding of both representational models (which understand language) and generative models (which create language).\u003c\/b\u003e While the industry frequently hyper-focuses on chatbots, the authors demonstrate how embeddings, vector spaces, and transformers can solve a massive array of enterprise problems—including semantic search, programmatic text clustering, topic modeling, and automated classification.\u003c\/p\u003e\n\u003cp data-path-to-node=\"8\" id=\"p-rc_9448da3ea72ee521-136\"\u003e\u003cspan class=\"citation-313 citation-end-313\"\u003eFeaturing nearly 300 custom, beautifully designed diagrams, the textbook maps the invisible mathematical pathways inside neural networks into clear visual flows.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e Instead of drowning the reader in raw calculus, it uses concrete Python code labs (optimized for free cloud hardware like Google Colab) using standard ecosystem libraries like PyTorch, Hugging Face \u003ccode data-path-to-node=\"8\" data-index-in-node=\"360\"\u003etransformers\u003c\/code\u003e, and \u003ccode data-path-to-node=\"8\" data-index-in-node=\"378\"\u003esentence-transformers\u003c\/code\u003e. \u003cspan class=\"citation-312\"\u003eThe curriculum takes a pragmatic approach: it starts with how text is sliced into tokens, reveals what happens inside attention heads, and builds up to production-grade patterns like \u003c\/span\u003e\u003cb data-path-to-node=\"8\" data-index-in-node=\"584\"\u003e\u003cspan class=\"citation-312\"\u003eRetrieval-Augmented Generation (RAG)\u003c\/span\u003e\u003c\/b\u003e\u003cspan class=\"citation-312 citation-end-312\"\u003e and parameter-efficient fine-tuning.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs corporate development departments, local software startups, and enterprise data teams rush to integrate generative AI into their products, engineers often hit a practical wall. Relying on basic API tutorials or surface-level prompt guides frequently results in fragile codebases that struggle with token limits, produce erratic text variations, or balloon cloud costs due to poorly optimized workflows.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\" id=\"p-rc_9448da3ea72ee521-140\"\u003e\u003cspan class=\"citation-304\"\u003e\u003c\/span\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003e\u003cspan class=\"citation-304\"\u003eHands-On Large Language Models\u003c\/span\u003e\u003c\/i\u003e\u003cspan class=\"citation-304 citation-end-304\"\u003e provides the explicit, production-vetted, and highly accessible blueprint that development teams need.\u003csup class=\"superscript\" data-turn-source-index=\"12\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-303 citation-end-303\"\u003eJay Alammar and Maarten Grootendorst replace dry academic theories with clear visual layouts, complete Jupyter notebook code labs, and real-world open-source applications.\u003csup class=\"superscript\" data-turn-source-index=\"13\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e By showing engineers exactly how to control text generation parameters, scale semantic search databases, and fine-tune models safely on custom datasets, this O'Reilly volume bridges the gap between raw research and stable, production-grade applications. It is an indispensable manual for any modern AI engineering lab.\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: Applied Artificial Intelligence\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\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":47234103673017,"sku":null,"price":390.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Hands-On_Large_Language_Models.jpg?v=1779371008","url":"https:\/\/royalbooksbd.com\/products\/hands-on-large-language-models","provider":"Royal Books BD","version":"1.0","type":"link"}