{"title":"Computers - Artificial Intelligence - Computer Vision \u0026 Pattern Recognition","description":"\u003cp\u003eDive into top artificial intelligence books, computer vision, and pattern recognition titles. Shop the best books on AI, computer science, networking, and programming at our online bookstore today!\u003c\/p\u003e","products":[{"product_id":"nexus-a-brief-history-of-information-networks-from-the-stone-age-to-ai-9780593734223","title":"Nexus: A Brief History of Information Networks from the Stone Age to AI","description":"\u003cb\u003e#1 \u003ci\u003eNEW YORK TIMES \u003c\/i\u003eBESTSELLER - From the author of \u003ci\u003eSapiens\u003c\/i\u003e comes the groundbreaking story of how information networks have made, and unmade, our world. \u003c\/b\u003e\u003cp\u003e\u003c\/p\u003e\"Strikingly original . . . A historian whose arguments operate on the scale of millennia has managed to capture the zeitgeist perfectly.\"--\u003ci\u003eThe Economist\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003e\"This deeply important book comes at a critical time as we all think through the implications of AI and automated content production. . . . Masterful and provocative.\"--Mustafa Suleyman, author of \u003ci\u003eThe Coming Wave\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003eFor the last 100,000 years, we Sapiens have accumulated enormous power. But despite all our discoveries, inventions, and conquests, we now find ourselves in an existential crisis. The world is on the verge of ecological collapse. Misinformation abounds. And we are rushing headlong into the age of AI--a new information network that threatens to annihilate us. For all that we have accomplished, why are we so self-destructive? \u003cp\u003e\u003c\/p\u003e\u003ci\u003eNexus\u003c\/i\u003e looks through the long lens of human history to consider how the flow of information has shaped us, and our world. Taking us from the Stone Age, through the canonization of the Bible, early modern witch-hunts, Stalinism, Nazism, and the resurgence of populism today, Yuval Noah Harari asks us to consider the complex relationship between information and truth, bureaucracy and mythology, wisdom and power. He explores how different societies and political systems throughout history have wielded information to achieve their goals, for good and ill. And he addresses the urgent choices we face as non-human intelligence threatens our very existence. \u003cp\u003e\u003c\/p\u003eInformation is not the raw material of truth; neither is it a mere weapon. \u003ci\u003eNexus\u003c\/i\u003e explores the hopeful middle ground between these extremes, and in doing so, rediscovers our shared humanity.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Yuval Noah Harari\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Random House\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/10\/2024\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 528\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.80lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.30h x 6.30w x 1.70d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9780593734223\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eReview Citation(s): \u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLibrary Journal\u003c\/i\u003e 04\/01\/2024 pg. 13\u003cbr\u003e\u003ci\u003ePublishers Weekly\u003c\/i\u003e 08\/12\/2024\u003cbr\u003e\u003ci\u003eBooklist\u003c\/i\u003e 08\/01\/2024 pg. 7\u003cbr\u003e\u003ci\u003eKirkus Reviews\u003c\/i\u003e 08\/15\/2024\u003cbr\u003e\u003ci\u003eChoice\u003c\/i\u003e 07\/01\/2025\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003eProfessor\u003cb\u003e Yuval Noah Harari \u003c\/b\u003eis a historian, philosopher, and the bestselling author of \u003ci\u003eSapiens\u003c\/i\u003e \u003ci\u003eA Brief History of Humankind\u003c\/i\u003e, \u003ci\u003eHomo Deus: A Brief History of Tomorrow, 21 Lessons for the 21st Century\u003c\/i\u003e, and the series \u003ci\u003eSapiens: A Graphic History \u003c\/i\u003eand \u003ci\u003eUnstoppable Us\u003c\/i\u003e. 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It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Marc Peter Deisenroth,A. 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Deisenroth was Program Chair of the European Workshop on Reinforcement Learning (EWRL) 2012 and Workshops Chair of Robotics Science and Systems (RSS) 2013. His research received Best Paper Awards at the International Conference on Robotics and Automation (ICRA) 2014 and the International Conference on Control, Automation and Systems (ICCAS) 2016. In 2018, he was awarded the President's Award for Outstanding Early Career Researcher at Imperial College London. He is a recipient of a Google Faculty Research Award and a Microsoft P.hD. grant.\u003cb\u003e\u003ci\u003eFaisal, A. Aldo:\u003c\/i\u003e\u003c\/b\u003e - A. Aldo Faisal leads the Brain and Behaviour Lab at Imperial College London, where he is faculty at the Departments of Bioengineering and Computing and a Fellow of the Data Science Institute. He is the director of the 20Mio£ UKRI Center for Doctoral Training in AI for Healthcare. Faisal studied Computer Science and Physics at the Universität Bielefeld (Germany). 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With over 30 years of experience in the publishing industry, Brian pursues a passion for making computing and technology accessible to people of all ages and backgrounds. Brian also enjoys creating electronic devices that interact with the physical world. Outside of work, Brian collaborates with like-minded artists, technologists, and nonprofits in Rhode Island to create experiences that get people excited about STEM\/STEAM careers and hobbies.\u003cb\u003e\u003ci\u003eHattersley, Lucy:\u003c\/i\u003e\u003c\/b\u003e - \u003cb\u003eLucy Hattersley\u003c\/b\u003e is a technology writer specialising in programming, hardware, and maker culture. She is Editor of \u003ci\u003eRaspberry Pi Official Magazine\u003c\/i\u003e where she commissions, edits, and shapes articles for one of the UK's most beloved hobbyist publications. She writes tutorials with hands-on expertise in AI hardware, machine learning, and deploying local language models. An advocate for privacy-conscious technology, she also volunteers with FoodCycle in London and helps organise the Brockley Max community arts festival. In quieter moments she can be found reading actual books and wrestling with a cryptic crossword.","brand":"booksdeli.com","offers":[{"title":"Lucy Hattersley \/ Paperback \/ English","offer_id":48791146922141,"sku":"9781916868427","price":27.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0619\/5648\/9373\/files\/img_b1faf0d6-a721-4032-808f-593eacf4bbb7.jpg?v=1786549562"},{"product_id":"vision-language-models-building-vlms-with-hugging-face-9798341624047","title":"Vision Language Models: Building Vlms with Hugging Face","description":"\u003cp\u003eVision language models (VLMs) combine computer vision and natural language processing to create powerful systems that can interpret, generate, and respond in multimodal contexts. \u003cem\u003eVision Language Models\u003c\/em\u003e is a hands-on guide to building real-world VLMs using the most up-to-date stack of machine learning tools from Hugging Face, Meta (PyTorch), NVIDIA (Cuda), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farré, Andrés Marafioti, and Orr Zohar. 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Readers will learn how to prepare datasets, select the right architectures, fine-tune and deploy models, and apply them to real-world tasks across a range of industries.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eExplore core model architectures and alignment techniques\u003c\/li\u003e \u003cli\u003eTrain and fine-tune VLMs with Hugging Face, PyTorch, and others\u003c\/li\u003e \u003cli\u003eDeploy models for applications like image search and captioning\u003c\/li\u003e \u003cli\u003eImplement advanced inference strategies, from zero-shot to agentic systems\u003c\/li\u003e \u003cli\u003eBuild scalable VLM systems ready for production use\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Merve Noyan,Andrés Marafioti,Miquel Farré\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e O'Reilly Media\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 07\/14\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 406\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.43lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.19h x 7.00w x 0.84d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9798341624047\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003ci\u003eFarré, Miquel:\u003c\/i\u003e\u003c\/b\u003e - Miquel Farré is a video technology expert with over 15 years of experience and more than 60 patents in machine learning and information science. His career began at the Fraunhofer Institute, where he designed advanced video codecs, and Nagravision, where he developed video streaming security modules. Transitioning to video understanding, Miquel joined Disney to architect the enterprise content metadata platform, leading machine learning initiatives across Pixar, Marvel, Lucasfilm, ABC, and ESPN. He then moved to YouTube, driving search monetization before expanding his focus to lead monetization for the platform's Home and Watch Next surfaces. Before joining Studio Jadu, he worked at Hugging Face on video multimodal large language models and founded Arbro AI to build automated farming solutions.\u003cb\u003e\u003ci\u003eNoyan, Merve:\u003c\/i\u003e\u003c\/b\u003e - Merve Noyan is a machine learning engineer working in the ML advocacy engineering team at Hugging Face. She builds tools to enable people to build with vision language models across the Hugging Face ecosystem (transformers, TRL, smolagents). Previously she worked for different companies building natural language understanding based solutions on information retrieval and conversational agents.\u003cb\u003e\u003ci\u003eZohar, Orr:\u003c\/i\u003e\u003c\/b\u003e - Orr Zohar is a PhD candidate in SVL at Stanford University, advised by Professor Serena Yeung-Levy and supported by the Knight-Hennessy Scholarship. His research centers on large multimodal models, particularly in video understanding, with a focus on self-training methodologies and agentic design. Orr has co-developed innovative approaches such as Video-STaR, a self-training method for video instruction tuning, and VideoAgent, an agent-based framework for long-form video comprehension. 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