AI领域权威学者吴恩达的斯坦福论文教学课程，快快收藏！

“那些高效的机器学习研究者，都有什么样的习惯？”

其实，世界上从不缺少优秀的机器学习研究者，也不缺乏好的建议和指南。想要成为一名高效的机器学习研究者，最重要的是要有彻底的自我管理能力和一点坚持到底的决心，吴恩达在斯坦福 CS230 课程中专门开了一课，亲述了如何有效阅读论文，通过论文去了解一个新的领域。

吴恩达（Andrew Ng），1976年出生，斯坦福大学计算机科学系和电气工程系的副教授，人工智能实验室的主任，是人工智能和机器学习领域国际上最权威的学者之一。2007年，吴恩达获得斯隆奖（Sloan Fellowship）；2013年入选《时代》杂志年度全球最有影响力100人，成为16位科技界代表之一；2021年，被评选为“2021福布斯中国·北美华人精英TOP 60”。

挑选研究问题是需要品味的，可能选择正确问题的能力比技术技能更重要。随着研究的深入，我们会慢慢发现研究问题的各种滋味，能快速品味到哪些方法是有潜力的，哪些方法又是根本行不通的。

实际上，对于研究问题的品味，我们有好几种提升方法：

对于最后一点，我们可以自问「理论在什么时候是有用的」、「实验结果普遍吗」、「为什么一些方法广泛被采纳，其它又被遗忘」。前面三点都是从其它研究者获得信息，最后则是你自己的思考。

Resources can come in the form of research papers, Medium articles, blog posts, videos, GitHub repository etc.

资源可以是研究论文、媒体文章、博客文章、视频、GitHub知识库等形式。

A quick google search on the phrase “pose estimation” will provide you with top resources that contain information in regards to the subject matter. At this initial step, the aim is to collate all resources that are relevant。

在谷歌上搜索“pose estimate”，你会得到与主题相关的顶级资源。这一步的目标是整理所有相关的资源。

Ideally, at this stage, there is no limit to the number of resources you consider important, but be sure to create a shortlist of papers, videos and articles that are useful.

在这个阶段，对资源数量没有限制，但一定要创建一个名单列表，用于记录有用的论文、视频和文章。

吴恩达课堂笔记

It is crucial that there’s a method to track the understanding of each shortlisted resources. Andrew Ng, suggests a table of resource plotted against your understanding level that looks similar to the table below.

在这一步中，记录并跟踪对每个资源的理解程度是至关重要的。Andrew Ng建议根据对资源的理解程度绘制一个资源表格，它类似于下表。

It is advisable to ensure you go through at least 10–20% of the content of each paper you have added to the list; this will ensure that you have been exposed to enough of the introductory content within an identified resource and are able to gauge its relevancy accurately.

确保对添加到列表中的每篇论文至少有10-20%的理解；这将确保你已经对收集到的资源有了足够的整体性了解，并且能够准确地衡量它的相关性。

For the more relevant papers/resources identified, it is expected that you progress to a higher level of understanding. Eventually, you will have identified some appropriate resources with content that you understand fully.

You are probably asking yourself, “what number of papers/resource is sufficient”.

你可能会问，“多少论文/资源算足够呢”。

According to Andrew, an understanding of 5–20 papers will showcase a basic understanding within the subject matter, perhaps enough understanding to progress to implementation of techniques.

根据Andrew Ng的说法，理解了5-20篇论文的内容，那么你对该领域的研究就有了基本的理解，对该领域的相关技术也会有足够的理解。

50–100 papers will primarily provide you with a very good understanding of the domain.

50-100篇论文会让你对这个领域有一个更深入的理解。

After going through the resources and extraction of vital information, your table might look something similar to what’s shown below.

在掌握了该领域的一些重要资源后，你的表格可能就如下了。

The third step is to take structured notes that summarises the key discoveries, findings and techniques within a paper, in your own words.

做结构化的笔记，用你自己的话总结论文中的关键发现和技巧。

According to Andrew, reading a paper from the first word to the last word in one sitting might not be the best way to form an understanding.

根据Andrew Ng的说法，一口气的从第一个词读到最后一个词可能不是最好的方式。

Be prepared to go through a paper at least three times to have a good understanding of its content。

吴恩达强调：一篇论文至少要读三遍

In your first pass, start with reading the following sections within the paper: title, abstract and figures.

The second pass entails you reading the following sections: introduction, conclusion, another pass through figures and scan through the rest of the content.

The third pass of the paper involves reading the whole sections within the paper but skipping any complicated maths or technique formulations that might be alien to you. During this pass, you can also skip any terms and terminologies that you do not understand or aren’t familiar.

如若要深入理解一个领域，这些公式和术语还是必须搞懂的。

吴恩达课堂笔记

Andrew provides a set of questions that you should ask yourself as you read a paper. These questions generally will show you understand the critical information presented in a paper. I use the questions below as beacons to ensure I don’t stray from the aim of understanding vital information.

Andrew Ng提供了一组在阅读论文时应该问自己的问题。这些问题通常会表明你理解论文中提出的关键信息。我使用下面的问题作为指引，以确保不会偏离理解重要信息的目标。

论文的作者想要完成什么，或者已经完成了什么？

如果一篇论文介绍了一种新方法/技术/方法，那么该新方法的关键要素是什么?

论文中哪些内容对你有用?

你还想关注哪些参考文献?

吴恩达也强调：“Learn steadily rather than short burst for longevity.”

该博主根据Andrew Ng的方法，每个月至少阅读四篇研究论文，来达到对该领域的理解。随着对论文阅读频次的增加，阅读和理解文章的速度也会越来越快。

Andrew Ng states in his video that he carries a batch of research papers around with him, intending to read them.

在机器学习领域，每个人都会有自己的一套研究方法，一些好的建议和做法也并不一定适用于所有人。所以，如果想要成为一个高效的机器学习学习者，认清自己进而摸索出适合自己的道路至关重要。