Machine learning cheat sheet
Learning and science
machine learning reference
SHOW HN2011
How HN took it
3 positive, 2 neutral or asking, 1 mixed, 0 critical, out of 6 comments about the project. Thread score +0.33.
Docs: 1 for, 0 againstDesign: 1 for, 0 against
The thread, comment by comment
Each row is a top-level comment and its replies, in HN's order. Select a cell to open that comment on HN.
positive neutral or mixed critical about another commenter or something else the maker
What HN said
What people liked
Useful summary and format for learning ML
From 3 comments
Nice summary; I like the format as well. However, the title of the cheat sheet is misleading since (a) many of the algorithms listed can be used for non-linear classification and…
cloudkj on HNFantastic work, I have an ML exam coming up and this should really help. If I'm honest its one of the subjects I've struggled with the most. It seems experts in the field while…
ses on HNI'm taking this class next semester, downloaded it so hopefully I'll understand it later and it will come in use. Thanks!
axxl on HN
Looks cool
From 1 comment
No idea what i'm looking at but it definitely looks cool.
MatthewB on HN
Objections
Title was misleading since many algorithms are non-linear or supervised
From 1 comment
Nice summary; I like the format as well. However, the title of the cheat sheet is misleading since (a) many of the algorithms listed can be used for non-linear classification and…
cloudkj on HN
Missing algorithms and online learning column is incomplete
From 2 comments
All the algorithms requiring training can be optimized using stochastic gradient descent-- which is very effective for large data sets (see…
iskander on HNKNN "no learning involved": one probaby wants to cross-validate K at the least, if not learn the metric. Some methods say online learning isn't applicable. As pointed out…
imurray on HN
Requests
Add random forests and decision tree ensembles
From 1 comment
All the algorithms requiring training can be optimized using stochastic gradient descent-- which is very effective for large data sets (see…
iskander on HN
Make available in web form
From 1 comment
Nice summary; I like the format as well. However, the title of the cheat sheet is misleading since (a) many of the algorithms listed can be used for non-linear classification and…
cloudkj on HN
Questions
Why are random forests missing
From 1 comment
All the algorithms requiring training can be optimized using stochastic gradient descent-- which is very effective for large data sets (see…
iskander on HN
Alternatives named
Stanford online machine learning course
From 1 comment
In case you see the cheat sheet and think, "Wow, I'd love to understand that," there's an excellent (albeit challenging) complete course on machine learning in Stanford's…
teuobk on HN
Programming Collective Intelligence book
From 1 comment
Another option is "Programming Collective Intelligence," by Toby Segaran. I read through it recently on a long flight to Australia. It's one of the most straight-forward AI books…
natfriedman on HN
Elements of Statistical Learning
From 1 comment
I haven't read the COIN book, but if you want to get aggressive you can go for "Elements of Statistical Learning". Free pdf download, probably not a one-flight book:…
klochner on HN