{"title":"Computers - Data Science - Machine Learning","description":"\u003cp\u003eDiscover data science books, Python Data Science Handbook, machine learning books, and design patterns. Explore computer books, The Art of Computer Programming, networking, and bestsellers at our online bookstore!\u003c\/p\u003e","products":[{"product_id":"hbrs-10-must-reads-on-ai-with-bonus-article-how-to-win-with-machine-learning-by-ajay-agrawal-joshua-gans-and-avi-goldfarb-9781647825843","title":"Hbr's 10 Must Reads on AI (with Bonus Article How to Win with Machine Learning by Ajay Agrawal, Joshua Gans, and AVI Goldfarb)","description":"\u003cp\u003e\u003cb\u003eThe next generation of AI is here--use it to lead your business forward.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eIf you read nothing else on artificial intelligence and machine learning, read these 10 articles. 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He is an expert on digital transformation and organizational change. He advises senior leaders on how to organize their workforces to compete with data, analytics, and new technologies and consults with companies such as Google, Microsoft, General Motors, and Discover about using digital tools to enhance internal knowledge sharing, how to structure global product-development operations, and how to manage the human aspects of new-technology implementation. He is also a regular keynote speaker for corporate trainings and user conferences on digital transformation, innovation, and change. 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Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e - Hands-on, code-first learning\u003cbr\u003e - Comprehensive, from basics to generative AI\u003cbr\u003e - Intuitive and easy math explanations\u003cbr\u003e - Examples in Keras, PyTorch, JAX, and TensorFlow \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For readers with intermediate Python skills. 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A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn't open on the 2nd jump.","brand":"booksdeli.com","offers":[{"title":"Aur\u0026#195;\u0026#169;lien G\u0026#195;\u0026#169;ron \/ Paperback \/ English","offer_id":47474257756317,"sku":"9798341607989","price":112.48,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0619\/5648\/9373\/files\/img_174b04eb-487e-431f-bfd6-734d41d82246.jpg?v=1765395910"},{"product_id":"machine-learning-for-dummies-9781394373222","title":"Machine Learning for Dummies","description":"\u003cp\u003e\u003cb\u003eThe most human-friendly book on machine learning\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eSomewhere buried in all the systems that drive artificial intelligence, you'll find machine learning--the process that allows technology to build knowledge based on data and patterns. \u003ci\u003eMachine Learning For Dummies\u003c\/i\u003e is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. 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The result is a logically flowing book that remains hands-on, navigable, and technically deep without constantly interrupting the central build. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e - From-scratch implementations of core LLM reasoning improvements\u003cbr\u003e - Verifier-based evaluation methods\u003cbr\u003e - RL with automatic verifiers for mathematics tasks \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For readers who know Python and have some knowledge of machine learning. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eSebastian Raschka\u003c\/b\u003e is an LLM Research Engineer with over a decade of experience. He is the author of the bestselling book \u003ci\u003eBuild a Large Language Model (From Scratch).\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 1 Understanding reasoning models\u003cbr\u003e 2 Generating text with a pretrained LLM\u003cbr\u003e 3 Evaluating reasoning models\u003cbr\u003e 4 Improving reasoning with inference-time scaling\u003cbr\u003e 5 Inference-time scaling via self-refinement\u003cbr\u003e 6 Training reasoning models with reinforcement learning\u003cbr\u003e 7 Improving GRPO for reinforcement learning\u003cbr\u003e 8 Distilling reasoning models for efficient reasoning\u003cbr\u003e A References and further reading\u003cbr\u003e B Exercise solutions\u003cbr\u003e C Qwen3 LLM source code\u003cbr\u003e D Using larger LLMs\u003cbr\u003e E Batching and throughput-oriented execution\u003cbr\u003e F Common approaches to model evaluation\u003cbr\u003e G Building a chat interface\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Sebastian Raschka\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Manning Publications\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/11\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 440\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 2.05lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.20h x 7.30w x 1.20d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781633434677\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eSebastian Raschka\u003c\/b\u003e has been working on machine learning and AI for more than a decade. Sebastian joined Lightning AI in 2022, where he now focuses on AI and LLM research, developing open-source software, and creating educational material. Prior to that, Sebastian worked at the University of Wisconsin-Madison as an assistant professor in the Department of Statistics, focusing on deep learning and machine learning research. He has a strong passion for education and is best known for his bestselling books on machine learning using open-source software.\u003cbr\u003e","brand":"booksdeli.com","offers":[{"title":"Sebastian Raschka \/ Paperback \/ English","offer_id":48867404054685,"sku":"9781633434677","price":95.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0619\/5648\/9373\/files\/img_70630f4d-dfe6-4926-b76b-bb001283ec4e.jpg?v=1788334668"},{"product_id":"build-an-ai-agent-from-scratch-agents-that-reason-plan-and-act-autonomously-9781633434615","title":"Build an AI Agent (from Scratch): Agents That Reason, Plan, and ACT Autonomously","description":"\u003cb\u003eGet the eBook free when you register your print book at Manning.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003e\"A practical guide for anyone who wants to move beyond demos and create agentic systems with confidence.\"\u003cbr\u003e--Elliot Kim, Coupang Inc.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eThis book is a fascinating step-by-step journey through design, development, and deployment of a system of autonomous AI agents. 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As one reviewer remarked, even for someone building their first agent, \"nothing feels like magic or a mystery after the first few chapters!\" \u003cp\u003e\u003c\/p\u003eEarly on, you'll establish the foundational blueprint for your agent by distinguishing rigid developer-defined workflows from true, LLM-directed agent loops. You'll learn how to manage stateless APIs, enforce structured outputs using Pydantic, and implement dynamic tool-calling. Crucially, the book centers on \"Context Engineering\"--the discipline of systematically structuring information to prevent context rot and the \"Lost in the Middle\" effect. One technical reviewer particularly appreciated that core concepts like agent loops, context engineering, and evaluation are introduced with clear motivation and practical examples rather than as isolated abstractions. \u003cp\u003e\u003c\/p\u003eAfter you build the basic agent, you'll progressively expand its capabilities by integrating custom web search, local file system exploration, and vector-based RAG to navigate complex data. You'll also enable the agent to recall past failures and adapt state persistence through hierarchical context optimization and longterm memory. Along the way, you'll see how to use the Model Context Protocol (MCP) to seamlessly connect your agent to external tools and servers. \u003cp\u003e\u003c\/p\u003eLater chapters push into advanced territory. To prevent the agent from hallucinating or looping during complex tasks, you'll implement explicit planning and procedural reflection. Then, you'll explore the Code-Act paradigm, empowering the agent with sandboxed cloud environments to securely write scripts, run CLI commands, and compose tools dynamically. Finally, you'll up-scale your project from a single agent into a collaborative, multi-agent architecture using the A2A protocol, where specialized tasks are routed to isolated sub-agents to maximize token efficiency. \u003cp\u003e\u003c\/p\u003eBecause highly autonomous agents carry real-world risks, the book culminates in a rigorous observability and evaluation pipeline. You'll implement an automated LLM-as-a-judge system--powered by Open Telemetry--to trace, grade, and continuously refine the agent's trajectory and outputs. By building every layer from the ground up, the book ensures that readers are left with a deep, practical understanding of AI mechanics. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e- End-to-end construction of a framework-free, autonomous AI agent in Python\u003cbr\u003e- Advanced practical integrations including MCP, vector-based RAG, long-term memory, and sandboxed code execution\u003cbr\u003e- Multi-agent orchestration and automated LLM-as-a-judge evaluation pipelines \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eFor Python developers with some knowledge of machine learning who want to build and deeply understand agentic systems without relying on abstract libraries. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003eJungjun Hur\u003c\/b\u003e is an independent AI and data engineer who specializes in building agentic systems for e-commerce. He is the author of \u003ci\u003ePractical AI Application Development Using LLMs\u003c\/i\u003e. \u003cb\u003eYounghee Song\u003c\/b\u003e is an AI consultant at PricewaterhouseCoopers (PwC). Both authors live and work in South Korea. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003ePart 1\u003cbr\u003e1 What is an AI agent?\u003cbr\u003e2 The brain of AI agents: LLMs\u003cbr\u003e3 Enabling actions: Tool use\u003cbr\u003e4 Implementing a basic ReAct agent\u003cbr\u003ePart 2\u003cbr\u003e5 Building knowledge bases with RAG\u003cbr\u003e6 Adding memory to your agent\u003cbr\u003e7 Planning and reflection for complex tasks\u003cbr\u003e8 Empowering agents with code execution\u003cbr\u003e9 Orchestrating multi-agent systems\u003cbr\u003e10 Evaluating agents\u003cbr\u003eA OpenAI API Key\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Jungjun Hur,Younghee Song\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Manning Publications\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/18\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 336\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.90lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.20h x 7.30w x 1.10d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781633434615\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eJungjun Hur\u003c\/b\u003e is an AI and data engineer with experience in e-commerce and AI industries, where he has built production-ready AI applications and LLM-powered features. He is the author of the bestselling book \u003ci\u003ePractical AI Application Development Using LLMs\u003c\/i\u003e. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eYounghee Song\u003c\/b\u003e is an AI consultant at PwC. She graduated from Hanyang University with a degree in Economics and Finance and holds a Master's in Human Factors Engineering from Seoul National University.\u003cbr\u003e","brand":"booksdeli.com","offers":[{"title":"Jungjun Hur \/ Paperback \/ English","offer_id":48909858963613,"sku":"9781633434615","price":95.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0619\/5648\/9373\/files\/img_3e821849-f716-4fab-8920-9e8db6d467f1.jpg?v=1789454115"},{"product_id":"practical-ai-security-a-hands-on-guide-to-attacking-defending-and-securing-modern-ai-systems-9781718504660","title":"Practical AI Security: A Hands-On Guide to Attacking, Defending, and Securing Modern AI Systems","description":"\u003cb\u003eBreak AI Systems. 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Every technique includes clear explanations and real-world examples, and you can run the attacks and defenses yourself with over 30 hands-on Python demos. \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand how different kinds of machine learning models create unique vulnerabilities, and explore how these models are integrated into more autonomous, agentic AI systems to introduce new weaknesses and risks.\u003c\/li\u003e\n\u003cli\u003eIdentify, exploit, and defend against dozens of weaknesses and attacks across the AI life cycle, including data poisoning, model theft, and prompt injection.\u003c\/li\u003e\n\u003cli\u003eEvaluate AI systems for safety failures, bias, and alignment risks using structured benchmarking.\u003c\/li\u003e\n\u003cli\u003eThreat-model agentic systems, RAG pipelines, and multimodal architectures using MITRE ATLAS, OWASP, and the MAESTRO framework.\u003c\/li\u003e\n\u003cli\u003eDesign and execute AI-specific red teaming campaigns, and understand what makes them distinct from traditional security tests.\u003c\/li\u003e\n\u003cli\u003eConduct rapid risk audits and navigate AI governance frameworks for real deployments.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eWhether you use, build, deploy, or oversee AI, this isn't niche knowledge--it's the foundation for defending the technologies that will define the next era of human progress.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Harriet Farlow\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e No Starch Press\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/09\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 392\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.60lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.10h x 6.90w x 1.10d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781718504660\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eHarriet Farlow\u003c\/b\u003e is the CEO and founder of Mileva Security Labs, Australia's first dedicated AI security company. 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