AI 与机器学习2018arxiv.org
面向工程师的机器学习简明导论（2017）
A Brief Introduction to Machine Learning for Engineers (2017)
进阶 · 约 180 分钟读完前置知识：线性代数、概率统计、微积分基础
导读
这篇 2017 年的 arXiv 文档旨在为工程师提供机器学习的简要入门，系统梳理该领域的主要概念和方法。有读者认为它覆盖面广，但对特征工程与特征选择的讨论不足，实际建模仍需额外学习。
HN 关注点：作为工程师入门综述的实用性，并由此讨论学习新范式的最佳时机、特征工程缺口以及技术资料的社区评价问题。
HN 评论精选
这几条评论里：质疑 2 补充 2
质疑建议等范式稳定后再投入学习，类比 JavaScript 框架的演化；但也承认可能错失早期红利。
这很好地说明了三年前不学机器学习是有道理的。如果你等足够久、让新范式稳定下来，就能在混乱的过渡期更明智地分配精力，比如花在人类知识中不随时间变化的部分——更深入地理解纯数学的某些部分，甚至哲学或历史——同时等待新的基础知识收敛、真正扎实的教学解释出现。类似地，也可以说应该跳过 JavaScript 框架的早期阶段：如果把时间用于学习今天仍然有用、未来几十年仍会有用的东西，等待行业四处跳跃后最终收敛到看起来相当稳健的稳态（React），那你今天可能比逐月追随炒作周期更好。反方观点是，你可能会错过很多金钱回报，这些回报来自学习别人没时间或意志力去攻克的高需求技能，尤其是在学习摩擦很大的时期。
tw1010查看英文原文
This makes a good case for not learning machine learning three years ago. If you wait long enough for new paradigms to coagulate, you can use your energy more wisely during the chaotic transient period (e.g. by spending your effort on time-invariant parts of human knowledge, such as gaining a deeper understanding of parts of pure mathematics, or even philosophy or history), while you wait for a new set of fundamentals to converge, and really solid pedagogical explanations to surface. The same could be said about skipping the earlier period of javascript frameworks. Had you used your time to learn things that are still useful today, and will remain useful decades in the future, while waiting for the industry to jump here and there, eventually converging to what seems to be a fairly robust steady state (react), then you'd probably be better off today than if you followed along with the hype cycle month after month. A counter argument is that you might miss out on a lot of the monetary rewards that comes from learning the much desired skill that others don't have the time or will power to tackle in the period where doing so involves a lot of friction.
质疑内容全面，但特征工程与特征选择几乎缺失，而这是实际建模的关键。
这看起来是一份详尽的机器学习综述。尽管覆盖了很多基础，但我希望它更多讲如何系统地创建或选择特征。特征提取只有一段，而特征工程和特征选择在 200 多页里几乎没怎么提！我没有机器学习博士学位，但多年把它作为解决问题的工具。这里的细节能让你走很远，但不懂特征工程或特征选择，很难构建准确模型。对读完后想了解更多特征工程的工程师，我维护一个开源自动特征工程库 Featuretools（https://github.com/featuretools/featuretools），网站也有演示。
kmax12查看英文原文
This appears to be an thorough overview of machine learning. Even though many bases are covered, I wish there was more on how to create or select features for machine learning in a systematic way. Feature extraction gets 1 paragraph, but feature engineering and selection aren't mentioned much in the 200+ pages! I don't have a Phd in machine learning, but I have spent many years using it as a tool to solve problems. While the details here can get you a long way, without understand feature engineering or feature selection, you will have a hard time building accurate models. For any engineers looking for more on feature engineering after reading this, I maintain an open source library for automated feature engineering called Featuretools (https://github.com/featuretools/featuretools). We also have demos on our website (https://www.featuretools.com/demos) if you want to see it in action.
补充技术综述阅读成本高且内容重叠，需要社区评价与分类系统来帮助选择。
像这样的技术主题文档（和传统书籍一样）需要非常仔细地阅读，所需时间投入相对于人的寿命或职业生涯很大。而且它们与其它类似文档大量重叠，所以必须对读什么做出关键选择。最好能有一套高质量、广泛使用的社区评分：能添加新文档，按主题和水平自动分类，用社区投票（或许加上成熟预测器的自动评分）给每个文档在其覆盖的学科领域中的实用性/推荐度打分。arXiv 上有类似东西吗？人们会把一般性阐述材料（讲义、教科书等）放到 arXiv 吗？
Myrmornis查看英文原文
Documents like this (like traditional books) on technical subjects need to be read very carefully, requiring a time commitment that is large relative to the length of a human life or career. Also, they have a huge amount of overlap with other similar documents, so critical choices must be made about what to try to read. It would be nice to have a high-quality and widely-used set of community ratings for such documents. E.g. a place where - new documents can be added - they are categorized (automatically should be doable) according to subject matter, level, etc - some sort of community voting system (perhaps augmented by automated scoring by well-established predictors) scores each document for its utility/recommendability, in each of the subject areas that it covers. Does anything like that exist for arXiv? Do people put general expository material on arXiv? (E.g. lecture notes, textbooks, etc).
补充推荐一场面向游戏开发者的机器学习入门演讲，重点讲常见方法和陷阱。
GDC 2017 的 30 分钟演讲《像负责任的成年人一样应用机器学习》是面向游戏开发者的不错入门，介绍了常见机器学习方法和陷阱：https://www.youtube.com/watch?v=RLsKzkxWpK8
cpeterso查看英文原文
"Applying Machine Learning Like a Responsible Adult", a 30 minute talk from GDC 2017, is a nice introduction to common machine learning approaches and pitfalls for game developers: https://www.youtube.com/watch?v=RLsKzkxWpK8
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- 2018Hacker News 讨论帖379 分 · 38 评
- 2021Hacker News 讨论帖31 分 · 0 评
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