ndarray

.gitignore

@@ -19,3 +19,4 @@ build/mdd.zip
 /chapter_gluon-basics/mydict
 *egg-info*
 dist*
+_build/

chapter_preliminaries/index.md

@@ -0,0 +1,60 @@
+# 预备知识
+:label:`chap_preliminaries`
+
+
+To get started with deep learning,
+we will need to develop a few basic skills.
+All machine learning is concerned
+with extracting information from data.
+So we will begin by learning the practical skills
+for storing, manipulating, and preprocessing data.
+
+Moreover, machine learning typically requires
+working with large datasets, which we can think of as tables,
+where the rows correspond to examples
+and the columns correspond to attributes.
+Linear algebra gives us a powerful set of techniques
+for working with tabular data.
+We will not go too far into the weeds but rather focus on the basic
+of matrix operations and their implementation.
+
+Additionally, deep learning is all about optimization.
+We have a model with some parameters and
+we want to find those that fit our data *the best*.
+Determining which way to move each parameter at each step of an algorithm
+requires a little bit of calculus, which will be briefly introduced.
+Fortunately, the `autograd` package automatically computes differentiation for us,
+and we will cover it next.
+
+Next, machine learning is concerned with making predictions:
+what is the likely value of some unknown attribute,
+given the information that we observe?
+To reason rigorously under uncertainty
+we will need to invoke the language of probability.
+
+In the end, the official documentation provides
+plenty of descriptions and examples that are beyond this book.
+To conclude the chapter, we will show you how to look up documentation for
+the needed information.
+
+This book has kept the mathematical content to the minimum necessary
+to get a proper understanding of deep learning.
+However, it does not mean that
+this book is mathematics free.
+Thus, this chapter provides a rapid introduction to
+basic and frequently-used mathematics to allow anyone to understand
+at least *most* of the mathematical content of the book.
+If you wish to understand *all* of the mathematical content,
+further reviewing the [online appendix on mathematics](https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html) should be sufficient.
+
+```toc
+:maxdepth: 2
+
+ndarray
+pandas
+linear-algebra
+calculus
+autograd
+probability
+lookup-api
+```

chapter_preliminaries/index_origin.md

@@ -0,0 +1,65 @@
+---
+source: https://github.com/d2l-ai/d2l-en/blob/master/chapter_preliminaries/index.md
+commit: 9bf95b1
+---
+
+#  Preliminaries
+:label:`chap_preliminaries`
+
+To get started with deep learning,
+we will need to develop a few basic skills.
+All machine learning is concerned
+with extracting information from data.
+So we will begin by learning the practical skills
+for storing, manipulating, and preprocessing data.
+
+Moreover, machine learning typically requires
+working with large datasets, which we can think of as tables,
+where the rows correspond to examples
+and the columns correspond to attributes.
+Linear algebra gives us a powerful set of techniques
+for working with tabular data.
+We will not go too far into the weeds but rather focus on the basic
+of matrix operations and their implementation.
+
+Additionally, deep learning is all about optimization.
+We have a model with some parameters and
+we want to find those that fit our data *the best*.
+Determining which way to move each parameter at each step of an algorithm
+requires a little bit of calculus, which will be briefly introduced.
+Fortunately, the `autograd` package automatically computes differentiation for us,
+and we will cover it next.
+
+Next, machine learning is concerned with making predictions:
+what is the likely value of some unknown attribute,
+given the information that we observe?
+To reason rigorously under uncertainty
+we will need to invoke the language of probability.
+
+In the end, the official documentation provides
+plenty of descriptions and examples that are beyond this book.
+To conclude the chapter, we will show you how to look up documentation for
+the needed information.
+
+This book has kept the mathematical content to the minimum necessary
+to get a proper understanding of deep learning.
+However, it does not mean that
+this book is mathematics free.
+Thus, this chapter provides a rapid introduction to
+basic and frequently-used mathematics to allow anyone to understand
+at least *most* of the mathematical content of the book.
+If you wish to understand *all* of the mathematical content,
+further reviewing the [online appendix on mathematics](https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html) should be sufficient.
+
+```toc
+:maxdepth: 2
+
+ndarray
+pandas
+linear-algebra
+calculus
+autograd
+probability
+lookup-api
+```
+