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The Little Learner (A Straight Line to Deep Learning)

List Price: $55.00
SKU:
9780262546379
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  • Product Details

    Author:
    Daniel P. Friedman, Anurag Mendhekar, Qingqing Su, Guy L. Steele, Jr., Peter Norvig
    Format:
    Paperback
    Pages:
    440
    Publisher:
    MIT Press (February 21, 2023)
    Language:
    English
    ISBN-13:
    9780262546379
    ISBN-10:
    026254637X
    Weight:
    29oz
    Dimensions:
    7.06" x 9" x 1.05"
    File:
    RandomHouse-PRH_Book_Company_PRH_PRT_Onix_full_active_D20260405T171153_155746855-20260405.xml
    Folder:
    RandomHouse
    List Price:
    $55.00
    Case Pack:
    14
    As low as:
    $42.35
    Publisher Identifier:
    P-RH
    Discount Code:
    A
    QuickShip:
    Yes
    Audience:
    General/trade
    Country of Origin:
    United States
    Pub Discount:
    65
    Imprint:
    The MIT Press
  • Overview

    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