The Little Learner: A Straight Line to Deep Learning (Paperback) (小小學習者：通往深度學習的直線之路)

Friedman, Daniel P., Mendhekar, Anurag, Su, Qingqing

The Little Learner: A Straight Line to Deep Learning (Paperback)
  • 出版商: Summit Valley Press
  • 出版日期: 2023-02-21
  • 售價: $1,980
  • 貴賓價: 9.5 折 $1,881
  • 語言: 英文
  • 頁數: 440
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 026254637X
  • ISBN-13: 9780262546379
  • 相關分類: DeepLearning

    • 立即出貨 (庫存 < 3)

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商品描述

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

商品描述(中文翻譯)

《小小學習者》是一本以問答形式撰寫的深度學習入門書籍，以易於理解的方式詳細介紹深度學習的概念和步驟。

本書以幽默風趣的語言和蘇格拉底式的教學方法，從基礎原理開始逐步構建深度神經網絡，並使用一系列小型程式相互補充。從零開始，讀者將逐步實現一個重要應用：一個能識別噪聲摩斯電碼信號的系統。《小小學習者》以實例驅動，易於理解，涵蓋了深度神經網絡運作的所有概念，包括張量、擴展運算符、梯度下降算法、人工神經元、密集網絡、卷積網絡、殘差網絡和自動微分等。

本書以對話式風格、插圖和問答形式呈現，使深度學習變得易於理解和有趣。逐步構建的方法從基礎原理開始，逐步引入高級概念。本書使用Scheme語言的一小部分，介紹機器學習的關鍵思想。適合具備高中數學知識和一些編程經驗的讀者閱讀。

作者簡介

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.

作者簡介(中文翻譯)

Daniel P. Friedman 是印第安納大學資訊學院的計算機科學教授，也是麻省理工學院出版社出版的多本書籍的作者，包括《The Little Schemer》和《The Seasoned Schemer》（與Matthias Felleisen合著）、《The Little Prover》（與Carl Eastlund合著）以及《The Reasoned Schemer》（與William E. Byrd、Oleg Kiselyov和Jason Hemann合著）。

Anurag Mendhekar 是Paper Culture的聯合創始人和總裁，他專注於開發用於創意的人工智能，同時也是一位企業家。他的職業生涯始於施樂公司的帕羅奧圖研究中心（PARC），在那裡他是面向方面的編程的發明者之一。他的職業生涯涵蓋了分散系統、圖像和視頻壓縮以及虛擬現實的視頻分發等多個技術領域。

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