5 ms·
A Machine Learning cheatsheet focusing on the most popular algorithms
- mtb2718 8y agoMisleading right off the bat. “Linear” and “parametric” are orthogonal concepts. A parametric model can be linear or non-linear (I.e you can fit the parameters of a model f(x) = A * sin(w*x)). A non-parametric model can also be a linear combination of examples, or not.
- lihaciudaniel 8y agoSince this will become obsolete in 5 years of sooner, it's just a great introduction. Not something hanging in my room/office.
- curiousgal 8y agoMachine Learning is merely a bunch of statistical algorithms that have been around for centuries. So although the name/branding may indeed change in a few years, the content will be the same.
- karmakaze 8y agoAlphaGo changed my worldview. It was the first sort of singularity that divides my history of the world into before and after. Now I don't doubt that we will rapidly continue to develop the algorithms and hw too that is all very different from what is believed to be known.
- physicsyogi 8y ago"Centuries" is a bit of a stretch, right? Or am I misunderstanding your comment? Some of the most widely used algorithms in machine learning, for training or prediction, are much younger than that: - backpropagation (1970s) - Viterbi algorithm (1960s) - Markov Chain Monte Carlo (1940s) (Though, the Buffon's needle problem is from the 18th century.) And things like conditional random fields, LDA, and LSTMs, were introduced after 2000. edit: removed lone parenthesis
- Raphaellll 8y agoLSTM 1997 https://dl.acm.org/citation.cfm?id=1246450 https://dl.acm.org/citation.cfm?id=1246450
- curiousgal 8y agoI was referring mostly to Bayes, PCA and the regression algorithms which are widely used.
- dannykwells 8y agoNothing included about cross-validation which is probably the most important concept in modern machine learning. Not sure what to think about that.
- jczhang 8y agoCould you elaborate? Any good resources on that?
- ryanmonroe 8y agoIn the Ordinary Least Squares section, this sheet says the vector of residuals is equal to the sum of the squared elements of that vector.