Deep Learning Notes: Chapter 1 Introduction

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

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