深度学习所需的矩阵演算
The Matrix Calculus You Need For Deep Learning
摘要：
本文试图解释你需要的所有矩阵演算，以便理解深度神经网络的训练。我们假设没有数学知识超过你在微积分1中学到的，并提供链接，以帮助您刷新必要的数学需要。请注意，在开始学习训练并在实践中使用深度学习之前，您不需要理解本材料;相反，这本书是为那些已经熟悉神经网络基础知识，并希望加深对底层数学理解的人准备的。如果你在学习过程中遇到了困难，不要担心--只要回去重读上一节，试着写下来并练习一些例子。如果你仍然被卡住了，我们很乐意在forums.fast.ai的理论类别中回答你的问题。注：在文章的结尾有一个参考部分，总结了这里讨论的所有关键矩阵演算规则和术语。相关文章见http://explained.ai
英文摘要：
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don't worry if you get stuck at some point along the way---just go back and reread the previous section, and try writing down and working through some examples. And if you're still stuck, we're happy to answer your questions in the Theory category at forums.fast.ai. Note: There is a reference section at the end of the paper summarizing all the key matrix calculus rules and terminology discussed here. See related articles at http://explained.ai
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
