{"product_id":"the-little-learner-a-straight-line-to-deep-learning-9780262546379","title":"The Little Learner: A Straight Line to Deep Learning","description":"\u003cb\u003eA highly accessible, step-by-step introduction to deep learning, written in an engaging, question-and-answer style.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003ci\u003eThe Little Learner\u003c\/i\u003e introduces deep learning from the bottom up, inviting students to learn by doing. With the characteristic humor and Socratic approach of classroom favorites \u003ci\u003eThe Little Schemer\u003c\/i\u003e and \u003ci\u003eThe Little Typer, \u003c\/i\u003e this kindred text explains the workings of deep neural networks by constructing them incrementally from first principles using little programs that build on one another. Starting from scratch, the reader is led through a complete implementation of a substantial application: a recognizer for noisy Morse code signals. Example-driven and highly accessible, \u003ci\u003eThe Little Learner\u003c\/i\u003e covers all of the concepts necessary to develop an intuitive understanding of the workings of deep neural networks, including tensors, extended operators, gradient descent algorithms, artificial neurons, dense networks, convolutional networks, residual networks, and automatic differentiation. \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eConversational style, illustrations, and question-and-answer format make deep learning accessible and fun\u003c\/li\u003e\n\u003cli\u003eIncremental approach constructs advanced concepts from first principles\u003c\/li\u003e\n\u003cli\u003ePresents key ideas of machine learning using a small, manageable subset of the Scheme language\u003c\/li\u003e\n\u003cli\u003eSuitable for anyone with knowledge of high school math and some programming experience\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=AUTH-16595858\"\u003eDaniel P. Friedman\u003c\/a\u003e, \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=AUTH-15603623\"\u003eAnurag Mendhekar\u003c\/a\u003e\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e MIT Press\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 02\/21\/2023\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 440\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.50lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 8.90h x 6.93w x 1.26d\u003cbr\u003e\u003cb\u003eISBN13:\u003c\/b\u003e 9780262546379\u003cbr\u003e\u003cb\u003eISBN10:\u003c\/b\u003e 026254637X\u003cbr\u003e\u003cb\u003eBISAC Categories:\u003c\/b\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-COM\"\u003eComputers\u003c\/a\u003e | \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-COM094000\"\u003eData Science | Machine Learning\u003c\/a\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-COM\"\u003eComputers\u003c\/a\u003e | \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-COM004000\"\u003eArtificial Intelligence | General\u003c\/a\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-COM\"\u003eComputers\u003c\/a\u003e | \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-COM051300\"\u003eProgramming | Algorithms\u003c\/a\u003e\u003cbr\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDaniel P. Friedman\u003c\/b\u003e is Professor of Computer Science in the School of Informatics, Computing, and Engineering at Indiana University and is the author of many books published by the MIT Press, including \u003ci\u003eThe Little Schemer\u003c\/i\u003e and \u003ci\u003eThe Seasoned Schemer \u003c\/i\u003e(with Matthias Felleisen); \u003ci\u003eThe Little Prover\u003c\/i\u003e (with Carl Eastlund); and \u003ci\u003eThe Reasoned Schemer\u003c\/i\u003e (with William E. Byrd, Oleg Kiselyov, and Jason Hemann). \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cb\u003eAnurag Mendhekar\u003c\/b\u003e is Cofounder and President of Paper Culture, where he focuses on developing artificial intelligence for creativity, and an entrepreneur. He started his career at Xerox´s Palo Alto Research Center (PARC), where he was one of the inventors of aspect-oriented programming. His career has spanned a range of technologies including distributed systems, image and video compression, and video distribution for VR.","brand":"MIT Press","offers":[{"title":"Default Title","offer_id":45876711391407,"sku":"9780262546379","price":91.67,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0651\/9255\/8767\/files\/img_aceac5cd-346b-4280-9581-cf00701be434.jpg?v=1757694179","url":"https:\/\/www.correctionsbookstore.com\/es\/products\/the-little-learner-a-straight-line-to-deep-learning-9780262546379","provider":"Corrections Bookstore ","version":"1.0","type":"link"}