Deep Learning Architectures: A Mathematical Approach (Paper cover)
暫譯: 深度學習架構：數學方法 (平裝)

Calin, Ovidiu

Deep Learning Architectures: A Mathematical Approach (Paper cover)

商品描述

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.

This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

 

 

 

 

商品描述(中文翻譯)

這本書從數學的角度描述了神經網絡的運作。因此，神經網絡可以被解釋為函數的通用近似器和信息處理器。本書彌合了當前在直觀層面上使用的神經網絡的概念與現代數學語言之間的差距，展示了前者的最佳實踐，並享受後者的穩健性和優雅性。

這本書可以用於深度學習的研究生課程，前幾部分對於高年級本科生來說也相對容易理解。此外，這本書對於對該主題有理論理解興趣的機器學習研究人員也會有廣泛的吸引力。

作者簡介

Ovidiu Calin, a graduate from University of Toronto, is a professor at Eastern Michigan University and a former visiting professor at Princeton University and University of Notre Dame. He has delivered numerous lectures at several universities in Japan, Hong Kong, Taiwan, and Kuwait over the last 15 years. His publications include over 60 articles and 8 books in the fields of machine learning, computational finance, stochastic processes, variational calculus and geometric analysis.

作者簡介(中文翻譯)

奧維迪烏·卡林（Ovidiu Calin），多倫多大學畢業，現任東密歇根大學教授，曾擔任普林斯頓大學和聖母大學的訪問教授。在過去15年中，他在日本、香港、台灣和科威特的多所大學發表了許多講座。他的出版物包括60多篇文章和8本書，涵蓋機器學習、計算金融、隨機過程、變分微積分和幾何分析等領域。

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