深度学习的数学工程

English | 2024 | ISBN: 9781003298687 | 415 pages | True PDF,EPUB | 39.76 MB

Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning.

Key Features

A perfect summary of deep learning not tied to any computer language, or computational framework. An ideal handbook of deep learning for readers that feel comfortable with mathematical notation. An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains. The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials. Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.

英文| 2024 |国际标准图书编号：9781003298687 | 415页|真PDF，EPUB | 39.76 MB 深度学习的数学工程提供了使用数学语言进行深度学习的完整而简洁的概述。这本书提供了机器学习和优化算法的独立背景，并深入探讨了深度学习的关键思想。这些想法和架构包括深度神经网络、卷积模型、递归模型、长/短期记忆、注意力机制、变换器、变分自编码器、扩散模型、生成对抗网络、强化学习和图神经网络。概念是通过简单的数学方程以及对相关行业技巧的简洁描述来呈现的。这些内容是最先进的人工智能应用的基础，涉及图像、声音、大型语言模型和其他领域。重点是算法和方法的基本数学描述，不需要计算机编程。该报告对神经科学关系、历史观点和理论研究也持不可知论态度。这种简洁方法的好处是，具备数学能力的读者可以快速掌握深度学习的本质。 主要特点 深度学习的完美总结，与任何计算机语言或计算框架无关。对于熟悉数学符号的读者来说，这是一本理想的深度学习手册。对对视觉、声音、自然语言理解和科学领域产生影响的最具影响力的深度学习思想的最新描述。该论述与该领域的历史发展或神经科学无关，使读者能够快速掌握要点。深度学习很容易通过数学语言在许多专业人士都能达到的水平上进行描述。来自工程、统计学、物理学、纯数学、计量经济学、运筹学、定量管理、定量生物学、应用机器学习或应用深度学习等领域的读者将很快了解该领域的关键数学工程组成部分。

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