前言

最近开始读《Deep Learning》一书。这让我有了一个边读书边写笔记的动机：能够让人很轻松流畅的把握住这本书的脉络，从而读懂这本书的核心内容。
由于终究是英文表达更地道，因此该笔记都是节选自书中的原文。只有在我比较有把握的情况下才会给个别概念加上中文翻译。另外，“个人总结”部分是我自己的总结。各位读者如果有建议或意见，欢迎留言。谢谢！

Deep Learning Chapter 1 Introduction

Concept	Chinese	Description
Artificial Intelligence (AI)	人工智能	Intelligent software to automate routine labor, understand speech or images, make diagnoses in medicine and support basic scientific research.
Machine Learning	机器学习	AI systems acquire their own knowledge by extracting patterns from raw data.
Representation Learning	表示学习	Use machine learning to discover not only the mapping from representation to output but also the representation itself.
AI Deep Learning	AI 深度学习	Computers learn from experience and understand the world in terms of a hierarchy of concepts.

In the early days of AI, the field rapidly tackled and solved problems that are intellectually difficult for human beings but relatively straightforward for computers — problems that can be described by a list of formal (形式化), mathematical rules.
Reason: Abstract and formal tasks that are among the most difficult mental undertakings for a human being are among the easiest for a computer.

Challenge to AI: Problems that human solve intuitively, but hard to describe formally.
Example: Recognizing spoken words or faces in images.
Key challenge: How to get informal (非形式化) knowledge into a computer.
A solution: Machine learning.

Challenge to simple machine learning: The performance of simple machine learning algorithms depends heavily on the representation of the data they are given.
Key challenge: What features should be extracted. Feature (特征): The piece of information included in the representation.
A solution: Representation learning.

Goal of representation learning: To separate the factors of variation that explain the observed data. Factors: Sources of influence, can be thought of as concepts or abstractions that help us make sense of the rich variability of the data.

Challenge to representation learning: Disentangle the factors of variation and discard the ones that we do not care about.
A solution: Deep learning.

Method: Introducing representations that are expressed in terms of other, simpler representations.

Two main ways of measuring the depth of a mode:
1. The depth of the computational graph.
2. the depth of the graph describing how concepts are related to each other. It is used by deep probabilistic models,

个人总结

概念	输入	输出
Simple machine Learning	特征	最终结果
Representation Learning	原始数据	特征
Deep Learning	原始数据	多层次特征，就像一棵树，上一层特征是下一层特征的抽象。下一层特征更简单。

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Deep Learning Notes: Chapter 1 Introduction

前言

Deep Learning Chapter 1 Introduction

个人总结

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