Machine learning and other gibberish
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
#fun
The authors have got too many questions regarding Chinese translations....
Ref: https://www.deeplearningbook.org/
The authors have got too many questions regarding Chinese translations....
Ref: https://www.deeplearningbook.org/
介绍了一种基于粗略分类和顺序编号来整理数字内容的方法。分类的部分跟我目前整理文件的方式很类似，编号的做法则给我带来一些启发：令人联想到跟政府打交道时使用的一些表格，比如 I-140、1099-B 等。看似是随意的字符序列，但所有熟悉话题的人都明确它们是什么。
https://johnnydecimal.com/
https://johnnydecimal.com/
Johnnydecimal
A system to organise your life
Johnny.Decimal is a system to organise your life. Find things, quickly, with more confidence, and less stress. It's free to use and the concepts are the same at home or work.
#statistics
https://en.wikipedia.org/wiki/Dependent_and_independent_variables#Statistics_synonyms
The broken jargon system in statistics ...😱
> Depending on the context, an independent variable is sometimes called a "predictor variable", regressor, covariate, "controlled variable", "manipulated variable", "explanatory variable", exposure variable (see reliability theory), "risk factor" (see medical statistics), "feature" (in machine learning and pattern recognition) or "input variable." In econometrics, the term "control variable" is usually used instead of "covariate".
https://en.wikipedia.org/wiki/Dependent_and_independent_variables#Statistics_synonyms
The broken jargon system in statistics ...😱
> Depending on the context, an independent variable is sometimes called a "predictor variable", regressor, covariate, "controlled variable", "manipulated variable", "explanatory variable", exposure variable (see reliability theory), "risk factor" (see medical statistics), "feature" (in machine learning and pattern recognition) or "input variable." In econometrics, the term "control variable" is usually used instead of "covariate".
https://www.youtube.com/watch?v=KXRtNwUju5g
This is one of the hidden problems of our world. In some sense, the US is destroying the world. If you look at Germany, plastic recycling is much easier with all these machines in the stores. (or, is it?)
This is one of the hidden problems of our world. In some sense, the US is destroying the world. If you look at Germany, plastic recycling is much easier with all these machines in the stores. (or, is it?)
#ML
An interesting idea on time series predictions. Instead of predicting the exact time series, the author proposed a method to predict the future using ordinal patterns.
The figure shows how to disintegrate the time series into 8 overlapping short-term series (each with three numbers). To transform the short-term series into patterns, we write down the permutation pattern (for size of the series D=3, we have only 6 possible permutations). Then we will use the permutation patterns in the past to predict the patterns in the future.
BTW, this paper used the price of bitcoins as an example to test this method.
This method will not be super amazing. The point of this paper is to propose a simple method to predict the future using very limited resource.
This is the paper:
https://royalsocietypublishing.org/doi/10.1098/rsos.201011
Short-term prediction through ordinal patterns
An interesting idea on time series predictions. Instead of predicting the exact time series, the author proposed a method to predict the future using ordinal patterns.
The figure shows how to disintegrate the time series into 8 overlapping short-term series (each with three numbers). To transform the short-term series into patterns, we write down the permutation pattern (for size of the series D=3, we have only 6 possible permutations). Then we will use the permutation patterns in the past to predict the patterns in the future.
BTW, this paper used the price of bitcoins as an example to test this method.
This method will not be super amazing. The point of this paper is to propose a simple method to predict the future using very limited resource.
This is the paper:
https://royalsocietypublishing.org/doi/10.1098/rsos.201011
Short-term prediction through ordinal patterns
#ML
http://jibencaozuo.com/
PaperClip made a platform for everyone to play with artificial neural networks.
My impression: it looks nice. The interactions can be better but I am sure the next iteration will be much better.
http://jibencaozuo.com/
PaperClip made a platform for everyone to play with artificial neural networks.
My impression: it looks nice. The interactions can be better but I am sure the next iteration will be much better.
#cn
http://www.cddata.gov.cn/oportal/index
成都竟然有开放数据平台，而且做的还不错。
更新：
我发现有别的省市也有，难道是所有城市和省份已经统一了？都有这个开放数据平台？
http://www.cddata.gov.cn/oportal/index
成都竟然有开放数据平台，而且做的还不错。
更新：
我发现有别的省市也有，难道是所有城市和省份已经统一了？都有这个开放数据平台？
#TIL
https://stackoverflow.com/a/28142831/1477359
I had the same idea that git fast forward merge is more or less the same as rebase. Until I read this stackoverflow answer.
I guess we should always rebase whenever possible to maintain a clean history.
https://stackoverflow.com/a/28142831/1477359
I had the same idea that git fast forward merge is more or less the same as rebase. Until I read this stackoverflow answer.
I guess we should always rebase whenever possible to maintain a clean history.
#shameless
https://tools.kausalflow.com/
In the past years, I have been building a showcase of digital tools for academic researchers.
It started with some friends asking for recommendations of tools for reference management, visualization, note-taking, and so on ad infinitum.
So I built a GitHub repo to share what I have learned about these tools. This was way before the "awesome repo" concept. Later came the "GitHub awesome repo" shitstorm. Everyone is building an "awesome repo". I created a website for a better user experience to flee from the shitstorm.
Tools for Academic Research is a website for digital tool listings. At the moment, there are 154 tools listed. You can browse by tags or categories to find whatever you need. Or add an item (books, tools, reviews, etc) you love.
https://tools.kausalflow.com/
In the past years, I have been building a showcase of digital tools for academic researchers.
It started with some friends asking for recommendations of tools for reference management, visualization, note-taking, and so on ad infinitum.
So I built a GitHub repo to share what I have learned about these tools. This was way before the "awesome repo" concept. Later came the "GitHub awesome repo" shitstorm. Everyone is building an "awesome repo". I created a website for a better user experience to flee from the shitstorm.
Tools for Academic Research is a website for digital tool listings. At the moment, there are 154 tools listed. You can browse by tags or categories to find whatever you need. Or add an item (books, tools, reviews, etc) you love.
#career #business
[D] We Need More Data Engineers, Not Data Scientists
https://www.reddit.com/r/MachineLearning/comments/kx0j1v/d_we_need_more_data_engineers_not_data_scientists/
The report: https://www.mihaileric.com/posts/we-need-data-engineers-not-data-scientists/
[D] We Need More Data Engineers, Not Data Scientists
https://www.reddit.com/r/MachineLearning/comments/kx0j1v/d_we_need_more_data_engineers_not_data_scientists/
The report: https://www.mihaileric.com/posts/we-need-data-engineers-not-data-scientists/
In 2015, there was a company called SixFold. They were one of the first heroes to disrupt an industry that has not changed much for a century, the freight market. They investigated the situation, established their hypothesis, created MVP. They did not succeed. The image is a summary of their post mortem.
There are at least two learning from this story.
- Think in terms of the utility function. Do not just point out blocks of reasons. Write down the utility function for the situation and make assumptions on the parameters.
- Swarm intelligence sometimes works better than one might expect. Improvements in swarm intelligence take a lot of effort if one does not have a smart plan.
Here is the article by their CEO: https://medium.com/@MartKelder/end-of-road-for-trucking-startup-palleter-523a4a906fe9
#ML
https://alan-turing-institute.github.io/skpro/introduction.html#a-motivating-example
Alan Turing Institute created a package called skpro for probabilistic modeling. Unlike many other probabilistic modeling packages, skpro integrates into sklearn pretty well.
https://alan-turing-institute.github.io/skpro/introduction.html#a-motivating-example
Alan Turing Institute created a package called skpro for probabilistic modeling. Unlike many other probabilistic modeling packages, skpro integrates into sklearn pretty well.
