The Little Learner A Straight Line to Deep Learning

The Little Learner A Straight Line to Deep Learning by Daniel P. Friedman, published by MIT Press on February 21, 2023, offers a highly accessible introduction to deep learning. This 440-page book employs an engaging question-and-answer style to guide readers through the fundamentals of deep neural networks. By constructing these networks incrementally from first principles, the text invites students to learn through practical application, culminating in the development of a recognizer for noisy Morse code signals.
Readers will find that The Little Learner emphasizes an example-driven approach, making complex concepts in machine learning approachable. The book covers essential topics such as tensors, gradient descent algorithms, and convolutional networks, all while maintaining a conversational tone enhanced by illustrations. Designed for those with a background in high school mathematics and some programming experience, this edition serves as a practical resource for anyone interested in understanding the workings of deep learning and artificial intelligence.
Official synopsis Publisher
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
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