Read more
Informationen zum Autor Daniel P. Friedman 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 The Little Schemer and The Seasoned Schemer (with Matthias Felleisen); The Little Prover (with Carl Eastlund); and The Reasoned Schemer (with William E. Byrd, Oleg Kiselyov, and Jason Hemann). Anurag Mendhekar 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. Klappentext "A gentle but detailed introduction to some of the algorithmic ideas behind machine learning"-- Zusammenfassung A highly accessible, step-by-step introduction to deep learning, written in an engaging, question-and-answer style. The Little Learner introduces deep learning from the bottom up, inviting students to learn by doing. With the characteristic humor and Socratic approach of classroom favorites The Little Schemer and The Little Typer, 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, The Little Learner 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. Conversational style, illustrations, and question-and-answer format make deep learning accessible and fun Incremental approach constructs advanced concepts from first principles Presents key ideas of machine learning using a small, manageable subset of the Scheme language Suitable for anyone with knowledge of high school math and some programming experience Inhaltsverzeichnis Foreword by Guy L. Steele Jr. xi Foreword by Peter Norvig xiii Preface xix Transcribing to Scheme xxiii 0. Are You Schemish? 2 1. The Lines Sleep Tonight 18 2. The More We Learn, the Tenser We Become 30 Interlude I. The More We Extend, the Less Tensor We Get 46 3. Running Down a Slippery Slope 56 4. Slip-slidin' Away 72 Interlude II. Too Many Toys Make Us Hyperactive 92 5. Target Practice 98 Interlude III. The Shape of Things to Come 112 6. An Apple a Day 116 7. The Crazy "ates" 130 8. The Nearer Your Destination, the Slower You Become 144 Interlude IV. Smooth Operator 154 9. Be Adamant 162 Interlude V. Extensio Magnifico! 176 10. Doing the Neuron Dance 194 11. In Love with the Shape of Relu 212 12. Rock Around the Block 236 13. An Eye for an Iris 250 Interlude VI. How the Model Trains 270 Interlude VII. Are Your Signals Crossed? 282 14. It's Really Not That Convoluted 298 15. ...But It Is Correlated! 320 Epilogue. We've Only Just Begun 342 Appendix A. Ghost in the Machine 350 Appendix B. I Could Have Raced All Day 374 Acknowledgments 399 References 401 Index 402...
About the author
Daniel P. Friedman 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 The Little Schemer and The Seasoned Schemer (with Matthias Felleisen); The Little Prover (with Carl Eastlund); and The Reasoned Schemer (with William E. Byrd, Oleg Kiselyov, and Jason Hemann).
Anurag Mendhekar 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.
