The Little Learner

The Little Learner Image

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

Details e-Book The Little Learner

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🗸 Title: The Little Learner
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Readers' opinions about The Little Learner by Daniel P. Friedman, Anurag Mendhekar & Qingqing Su

I loved the powerful messages hidden within the story. The book tackled important social issues and made me ponder the world we live in. It's a must-read for everyone.

I loved the powerful messages hidden within the story. The book tackled important social issues and made me ponder the world we live in. It's a must-read for everyone.

I'm already planning to reread this book. It's one of those stories that you can revisit again and again, discovering new layers of meaning each time.

More by Daniel P. Friedman, Anurag Mendhekar & Qingqing Su

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