The Little Learner

Daniel P. Friedman

ISBN 10: 026254637X / ISBN 13: 9780262546379

Published by The MIT Press Feb 2023, 2023

About this Item

Neuware -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.

Seller Inventory # 9780262546379

Bibliographic Details

• Title: The Little Learner
• Publisher: The MIT Press Feb 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 026254637X
• ISBN 13: 9780262546379
• Binding: Taschenbuch
• Illustrator: Su, Qingqing
• Condition: Neu
• Dimensions: 221W x 179H x 30D millimeters 221 millimeters width by 179 millimeters height by 30 millimeters depth
• Weight: 823 grams

About this title

Synopsis

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

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.