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

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)

《深度學習數學基礎》提供了理解深度學習討論、探索更複雜實現方式以及更好地使用深度學習工具包所需的基本數學知識。

通過《深度學習數學基礎》，您將學習深度學習所使用的基本數學知識，並作為深度學習的背景知識。您將通過Python示例學習與深度學習相關的概率、統計學、線性代數、微分計算和矩陣計算等關鍵主題，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進行數學計算，並構建一個完全功能的神經網絡。

此外，您還將找到梯度下降的相關內容，包括深度學習社區常用的變體：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)

Ronald T. Kneusel在科羅拉多大學波德分校獲得機器學習博士學位。他擁有超過20年的機器學習行業經驗。Kneusel也是《Numbers and Computers》（第二版，Springer 2017）、《Random Numbers and Computers》（Springer 2018）和《Practical Deep Learning: A Python-Based Introduction》（No Starch Press 2021）的作者。

類似商品