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Machine Learning Algorithms Cheat Sheet
- deleted 5y ago[deleted]
- clircle 5y agoBlogspam
- nefitty 5y agoAccel.ai is a non-profit
- pc86 5y agoThese two things are not mutually exclusive. Despite the word count this is a pretty low-effort, superficial article.
- rsrsrs86 5y agoYes. It reads like other thousands of articles in medium or equivalent... Just content marketing hoping it gets some traffic.
- enjoyyourlife 5y agoSo is OpenAI
- rexreed 5y agoNot since 2019. From the wikipedia entry: "In 2019, OpenAI transitioned from non-profit to for-profit. The company distributed equity to its employees and partnered with Microsoft Corporation, who announced an investment package of US$1 billion into the company. OpenAI then announced its intention to commercially license its technologies, with Microsoft as its preferred partner."
- deleted 5y ago[deleted]
- rmbyrro 5y agoI don't get the purpose of these cheatsheets. Haven't we already automated these decisions and model optimization with AutoML? AI or Data Science is not my fulltime day job but my go to when I need any ML model is an AutoML library, such as AutoSklearn [1] or AutoKeras [2]. [1] https://automl.github.io/auto-sklearn/master/ https://automl.github.io/auto-sklearn/master/ [2] http://autokeras.com/ http://autokeras.com/
- discordance 5y agoAutoML is good most of the time but there is still a place for custom model development in particular applications where you to adjust and tune parameters.
- rmbyrro 5y agoI totally agree. Personally, I don't have the skills to tackle these particular cases. Cheatsheets don't help me in anything either. And I presume (maybe I'm wrong?) they're also useless for those with advanced skills for particular cases...
- yobbo 5y agoIt can take hours, days, or weeks, depending on data set, to even know if a model is going to work. In these cases, blindly searching for models in the entire space is sort of futile. But then, cheatsheets like these don't help much either.
- jll29 5y agoMachine learning isn't an exam (and if it was, it's an open book one), so nobody really needs cheat sheets. There is, however, a part of machine learning practice that is not covered by textbooks.
- pilotneko 5y agoStrange to see dimensionality reduction as the root node? I’m having a hard time reading this diagram.
- rsrsrs86 5y agoPresenting dimensionality reduction as the starting point in machine learning is not usual, but it does make sense, technically. A classification model can be seen as mapping inputs from an N dimensional space to set of labels. Regression maps N dimensional spaces to the real line and so on. Even a sample mean reduces a set with dimensionality equal to the sample size to a single real number.
- jrumbut 5y agoYou're right but really the sheet is just a mess. Naive Bayes is on both sides of "Data Too Large" and the wrong side of "Explainable." It's almost like they put some algorithm names and machine learning concepts in a hat and drew them at random.
- liara_k 5y agoReinforcement learning gets ignored again... :/
- amznbyebyebye 5y agoThey haven’t made it easy enough to understand yet
- oneoff786 5y agoReinforcement learning is rarely a good choice
- sgillen 5y agoYes but sometimes it’s your only choice.
- ivalm 5y agoFor what practical applications is it the only choice?
- utopcell 5y agoInformation Retrieval for one.
- king_magic 5y agoRL is definitely not the only option in the information retrieval space.
- swah 5y agoHey king, just curious - still do Crossfit 8 years later? ;) Asking here because you have no contact in your profile!
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- melony 5y agoToo much focus on implementation details and not enough emphasis on state of the art theory. A new practitioner should start with probabilistic learning for theory and deep learning on the practical side. A cursory overview of linear regression, decision trees, and the various linear algebra vector learning tricks is more than enough. They are mathematically interesting but not particularly useful except on sorted and cleaned tabular data. The best introduction to ML is by demonstrating its solutions to previously intractable classic problems: realistic text to speech, speech recognition, image classification, image generation, natural language processing. A boosted decision tree is rather limited in its applications (except for Kaggle competitions) that it should be something to learn after the fact. Understanding maximum likelihood and probability chaining at the information theoretic level is a lot more important than focusing on the nitty-gritty details of the Generalized Linear Model du jour. A good understanding of ideas like KL-divergence and messaging passing is more crucial than knowing which tree or forest algorithm to use. Many tutorials like to focus on linear and logistic regression because they are well understood and mathematically straightforward (for the educator). But machine learning is not purely a field of math, nor is it merely statistics. Too many practitioners never go beyond formula chugging and end up missing the forest for the trees. Sure a big enough model will solve almost any problem. However, without understanding the information theory and probabilistic explanations behind why a model can learn and function, ML will always remain a bag of tricks.
- pramodbiligiri 5y agoCan you suggest any resources for the information theoretic level explanation that you talk about? Do either of the Hastie-Tibshirani books cover it?
- melony 5y agohttps://probml.github.io/pml-book/ https://probml.github.io/pml-book/ Murphy's should be more than sufficient. Mackay (Information Theory, Inference, and Learning Algorithms) and Bishop (Pattern Recognition and Machine Learning) are also commonly recommended but their books are all more than a decade old.
- NalNezumi 5y agoOnly looking at the first graph, I'm curious why they left out "data size requirement". Does the problem you want to solve have access to a lot of data, or only very little (or data gathering is costly). Is it noisy? Biased? The latter might be beyond the scope, but the amount of data available seems to be crucial for the cheatsheet? Imagine following the graph and deciding on Neural network and then realize you can't collect enough data for it to work after. Looks like an low effort article to me
- montereynack 5y agoI agree, but I haven’t found any decent cheat sheet resources discussing problems in all these dimensions. Got any ideas? Open to textbooks as well.
- aquafox 5y agoSo many things wri g with it. PCA and SVD are placed on opposite nodes, but PCA is actually computed using SVD!
- ogogmad 5y agoI don't understand what use case they've given for SVD. Are they suggesting using it for compressing an image? Because that's a terrible idea, with an algorithmic complexity of O(n^3) and a very bad compression ratio. [edit] Just to underscore the point that you should never use SVD for compression except as a teaching example consider a comment here: https://mathoverflow.net/questions/408504/listing-applications-of-the-svd https://mathoverflow.net/questions/408504/listing-applicatio... The comment says That's a very nice way of getting some intuition about the SVD. But this is by no means an application of the SVD. Compression [sic] images this way is terrible by any standard (you get very bad image for still quite large storage and computing the compression is very slow). This is by an expert.
- ogogmad 5y agoTypo: "wri g" should be "wrong".
- samhw 5y agoAh, dammit, I thought he meant "wri g". This changes the meaning entirely. Well, I disagree with that, but I thought there was some real insight in the observation that there are many things wri g with it.
- deleted 5y ago[deleted]
- ogogmad 5y agoLooking through your comment history, you appear to have a history of passive-aggressive comments like the one above. A typo as extensive as "wri g" is pretty hard to read at first glance, because it has an intervening space.
- thibauts 5y agoUsed to use this cheat sheet https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html https://scikit-learn.org/stable/tutorial/machine_learning_ma...
- sireat 5y agoThat is the one I give for students who want to get started with ML. Has links to relevant papers for the implementations.
- minroot 5y agoWho the motherfucker decided to put a subscribe to mailing list form that blocks 1/3 of the screen on mobile?
- tpoacher 5y agoThis diagram is useful in two ways: 1. If you're an aspiring data scientist, you should know a little bit about each method mentioned there, at least. So it's a handy list. 2. It stimulated discussion on HN, with much better answers.
- deleted 5y ago[deleted]
- huqedato 5y agoVery useful, great AI/ML taxonomy. Tx