@antoniolupetti: "Dive into Deep Learning" is an open-source book that builds the mathematical foundations of large language models, cov…

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Summary

An open-source book that builds the mathematical foundations of large language models, covering linear algebra, calculus, probability, and transformer architectures, with over 1000 pages of clear explanations and practical examples.

"Dive into Deep Learning" is an open-source book that builds the mathematical foundations of large language models, covering topics such as linear algebra, calculus, probability, optimisation, backpropagation, attention mechanisms, and transformer architectures. The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. With more than 1000 pages, it includes very clear explanations, practical examples, and exercises, making it one of the best free resources for understanding the mathematical structure underlying modern AI systems and LLMs. https://arxiv.org/pdf/2106.11342
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Cached at: 05/21/26, 07:38 PM

“Dive into Deep Learning” is an open-source book that builds the mathematical foundations of large language models, covering topics such as linear algebra, calculus, probability, optimisation, backpropagation, attention mechanisms, and transformer architectures.

The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models.

With more than 1000 pages, it includes very clear explanations, practical examples, and exercises, making it one of the best free resources for understanding the mathematical structure underlying modern AI systems and LLMs.

https://arxiv.org/pdf/2106.11342

Dive into Deep Learning

Source: https://arxiv.org/abs/2106.11342 View PDF

Abstract:This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; (v) be complemented by a forum for interactive discussion of technical details and to answer questions.

Submission history

From: Aston Zhang [view email] **[v1]**Mon, 21 Jun 2021 18:19:46 UTC (26,020 KB) **[v2]**Mon, 26 Jul 2021 16:51:30 UTC (25,653 KB) **[v3]**Fri, 29 Jul 2022 17:10:12 UTC (34,593 KB) **[v4]**Fri, 10 Feb 2023 17:49:41 UTC (36,095 KB) **[v5]**Tue, 22 Aug 2023 17:02:42 UTC (38,577 KB)

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