{"product_id":"the-little-learner-isbn-9780262546379","title":"The Little Learner","description":"\u003cb\u003eA highly accessible, step-by-step introduction to deep learning, written in an engaging, question-and-answer style.\u003c\/b\u003e\u003cbr\u003e\u003cbr\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. \u003cbr\u003e\u003cbr\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\u003eForeword by Guy L. Steele Jr. xi\u003cbr\u003eForeword by Peter Norvig xiii\u003cbr\u003ePreface xix\u003cbr\u003eTranscribing to Scheme xxiii\u003cbr\u003e0. Are You Schemish? 2\u003cbr\u003e1. The Lines Sleep Tonight 18\u003cbr\u003e2. The More We Learn, the Tenser We Become 30\u003cbr\u003eInterlude I. The More We Extend, the Less Tensor We Get 46\u003cbr\u003e3. Running Down a Slippery Slope 56\u003cbr\u003e4. Slip-slidin' Away 72\u003cbr\u003eInterlude II. Too Many Toys Make Us Hyperactive 92\u003cbr\u003e5. Target Practice 98\u003cbr\u003eInterlude III. The Shape of Things to Come 112\u003cbr\u003e6. An Apple a Day 116\u003cbr\u003e7. The Crazy \"ates\" 130\u003cbr\u003e8. The Nearer Your Destination, the Slower You Become 144\u003cbr\u003eInterlude IV. Smooth Operator 154\u003cbr\u003e9. Be Adamant 162\u003cbr\u003eInterlude V. Extensio Magnifico! 176\u003cbr\u003e10. Doing the Neuron Dance 194\u003cbr\u003e11. In Love with the Shape of Relu 212\u003cbr\u003e12. Rock Around the Block 236\u003cbr\u003e13. An Eye for an Iris 250\u003cbr\u003eInterlude VI. How the Model Trains 270\u003cbr\u003eInterlude VII. Are Your Signals Crossed? 282\u003cbr\u003e14. It's Really Not That Convoluted 298\u003cbr\u003e15. ...But It Is Correlated! 320\u003cbr\u003eEpilogue. We've Only Just Begun 342\u003cbr\u003eAppendix A. Ghost in the Machine 350\u003cbr\u003eAppendix B. I Could Have Raced All Day 374\u003cbr\u003eAcknowledgments 399\u003cbr\u003eReferences 401\u003cbr\u003eIndex 402“Friedman's 'Little Books' are famous for teaching important topics in bite-sized, easily-digestible pieces. Now Dan and Anurag have turned their attention to machine learning, and they have succeeded masterfully.”\u003cbr\u003e\u003cb\u003e—Mitchell Wand, professor emeritus and part-time lecturer of computer science in the Khoury College of Computer Sciences, Northeastern University; co-author of \u003ci\u003eEssentials of Programming Languages\u003c\/i\u003e\u003c\/b\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).\u003cbr\u003e\u003cbr\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":"The MIT Press","offers":[{"title":"Default Title","offer_id":46300164751589,"sku":"NP9780262546379","price":55.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262546379.jpg?v=1767740273","url":"https:\/\/k12savings.com\/products\/the-little-learner-isbn-9780262546379","provider":"K12savings","version":"1.0","type":"link"}