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

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)

一個高度易於理解的逐步介紹深度學習的書籍，以引人入勝的問答風格撰寫。

《小學習者》從基礎開始介紹深度學習，邀請學生通過實作來學習。這本書延續了《小策劃者》和《小類型者》的幽默風格和蘇格拉底式的教學方法，通過逐步構建深度神經網絡，從基本原理開始，使用小程式相互建立。從零開始，讀者將被引導完成一個實質應用的完整實作：一個用於識別嘈雜摩斯碼信號的識別器。《小學習者》以範例為驅動，極具可讀性，涵蓋了發展對深度神經網絡運作的直觀理解所需的所有概念，包括張量（tensors）、擴展運算子（extended operators）、梯度下降算法（gradient descent algorithms）、人工神經元（artificial neurons）、密集網絡（dense networks）、卷積網絡（convolutional networks）、殘差網絡（residual networks）和自動微分（automatic differentiation）。

• 對話風格、插圖和問答格式使深度學習變得易於理解且有趣
• 漸進式方法從基本原理構建高級概念
• 使用小而可管理的Scheme語言子集呈現機器學習的關鍵概念
• 適合具備高中數學知識和一些程式設計經驗的任何人

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)

丹尼爾·P·弗里德曼是印第安納大學資訊學、計算與工程學院的計算機科學教授，也是多本由麻省理工學院出版社出版的書籍的作者，包括小型Scheme程式設計師和資深Scheme程式設計師（與馬提亞斯·費萊森合著）；小型證明者（與卡爾·伊斯特倫德合著）；以及理性Scheme程式設計師（與威廉·E·伯德、奧列格·基塞利約夫和傑森·赫曼合著）。

阿努拉格·門德卡爾是Paper Culture的共同創辦人兼總裁，專注於為創造力開發人工智慧，並且是一位企業家。他的職業生涯始於施樂的帕洛阿爾托研究中心（PARC），是面向方面編程的發明者之一。他的職業生涯涵蓋了多種技術，包括分散式系統、影像和視頻壓縮，以及虛擬實境的視頻分發。

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