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AI数学基础 Essential Math for AI PDF 下载
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相关截图：  资料简介 What Math Background Is Expected from You to Be Able to Read This Book?
This book is self-contained in the sense that we motivate everything that we
need to use. I do hope that you have been exposed to calculus and some
linear algebra, including vector and matrix operations, such as addition,
multiplication, and some matrix decompositions. I also hope that you
knowwhat a function is and how it maps an input to an output. Most of what we
do mathematically in AI involves constructing a function, evaluating a
function, optimizing a function, or composing a bunch of functions. You
need to know about derivatives (these measure how fast things change) and
the chain rule for derivatives. You do not necessarily need to know how to
compute them for each function, as computers, Python, Desmos, and/or
Wolfram|Alpha mathematics do a lot for us nowadays, but you need to
know their meaning. Some exposure to probabilistic and statistical thinking
are helpful as well. If you do not know any of the above, that is totally fine.
You might have to sit down and do some examples (from some other books)
on your own to familiarize yourself with certain concepts. The trick here is
to know when to look up the things that you do not know… only when you
need them, meaning only when you encounter a term that you do not
understand, and you have a good idea of the context within which it
appeared. If you are truly starting from scratch, you are not too far behind.
This book tries to avoid technicalities at all costs.
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