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Informationen zum Autor Daniel P. Friedman and Anurag Mendhekar, illustrated by Qingqing Su, foreword by Guy L. Steele Jr., foreword by Peter Norvig Klappentext 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 funIncremental approach constructs advanced concepts from first principlesPresents key ideas of machine learning using a small, manageable subset of the Scheme languageSuitable for anyone with knowledge of high school math and some programming experience 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...
