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I quickly (read everything, didn't solve the exercises, 5 days along several commutes and flights) skimmed Steinwart-Chritmann's Support Vector Machines before
by RBerenguel 10y ago
I quickly (read everything, didn't solve the exercises, 5 days along several commutes and flights) skimmed Steinwart-Chritmann's Support Vector Machines before joining my current company in case I needed to get into more modern papers, so I could have a decent-ish background in the subject. It's a tough area, and I have an acceptable level in functional analysis.
You are mixing "applying ML black boxes" with "creating ML black boxes". The former is not specially hard (if you know the language the libraries are in, have curiosity and time), but you need to know a little bit of what you are doing unless you want to just be one of the thousand monkeys with a typewriter. The latter is hard, like very hard.
- orthoganol 10y agoI'm not sure what you mean by "creating ML black boxes", since I don't think most people working in ML are required to invent new types of ML approaches, in the same way that Rails, Django, Sails works for most web apps. It's pretty straightforward to learn the ancillary data preparation, cross-validation, etc, and at the very least you can quickly apply a wide array of ML techniques (KNN, decision trees, LR, neural nets, etc.) with 3,4 LOC each (frameworks) and compare results, an approach you sometimes see on Kaggle. There's a reason "data science" bootcamps are putting out thousands of attendees on the job market - it's straightforward enough to get someone in the door for an interview after a few months of hard work (assuming they have either a strong programming or strong stats background).
- RBerenguel 10y agoBrief, on phone: my view of the comment addressed is that it is different doing stuff with ML that on ML, and I thought on was the addressed topic, not with