Math for Deep Learning: What You Need to Know to Understand Neural Networks (Paperback)
暫譯: 深度學習數學：理解神經網絡所需的知識

Kneusel, Ronald T.

Math for Deep Learning: What You Need to Know to Understand Neural Networks (Paperback)

買這商品的人也買了...

相關主題

商品描述

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.

With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.

You'll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You'll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.

In addition you'll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.

 

商品描述(中文翻譯)

**《深度學習的數學》**提供了理解深度學習討論、探索更複雜實現以及更好地使用深度學習工具包所需的基本數學知識。

透過**《深度學習的數學》**，您將學習深度學習所使用的基本數學及其背景知識。

您將通過Python範例學習與深度學習相關的關鍵主題，包括機率、統計、線性代數、微分計算和矩陣計算，以及如何在神經網絡中實現數據流、反向傳播和梯度下降。您還將使用Python來深入了解這些算法背後的數學，甚至構建一個功能完整的神經網絡。

此外，您將找到有關梯度下降的內容，包括深度學習社群常用的變體：隨機梯度下降（SGD）、Adam、RMSprop和Adagrad/Adadelta。

作者簡介

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder. He has over 20 years of machine learning industry experience. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017), Random Numbers and Computers (Springer 2018), and Practical Deep Learning: A Python-Based Introduction (No Starch Press 2021).

作者簡介(中文翻譯)

羅納德·T·克紐塞爾於科羅拉多大學博爾德分校獲得機器學習博士學位。他擁有超過20年的機器學習產業經驗。克紐塞爾也是《數字與計算機》（第二版，Springer 2017）、《隨機數與計算機》（Springer 2018）以及《實用深度學習：基於Python的入門》（No Starch Press 2021）的作者。

類似商品