Description: Little Learner : A Straight Line to Deep Learning, Paperback by Friedman, Daniel P.; Mendhekar, Anurag; Su, Qingqing (ILT); Steele, Guy L. (FRW); Norvig, Peter (AFT), ISBN 026254637X, ISBN-13 9780262546379, Brand New, Free shipping in the US 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
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Book Title: Little Learner : A Straight Line to Deep Learning
Item Length: 9in
Item Height: 1in
Item Width: 7in
Author: Anurag Mendhekar, Daniel P. Friedman
Publication Name: Little Learner : a Straight Line to Deep Learning
Format: Hardcover
Language: English
Publisher: MIT Press
Publication Year: 2023
Type: Textbook
Item Weight: 29.2 Oz
Number of Pages: 440 Pages