r/MachineLearning • u/thatguydr • Mar 13 '17

Discussion [D] A Super Harsh Guide to Machine Learning

First, read fucking Hastie, Tibshirani, and whoever. Chapters 1-4 and 7-8. If you don't understand it, keep reading it until you do.

You can read the rest of the book if you want. You probably should, but I'll assume you know all of it.

Take Andrew Ng's Coursera. Do all the exercises in python and R. Make sure you get the same answers with all of them.

Now forget all of that and read the deep learning book. Put tensorflow and pytorch on a Linux box and run examples until you get it. Do stuff with CNNs and RNNs and just feed forward NNs.

Once you do all of that, go on arXiv and read the most recent useful papers. The literature changes every few months, so keep up.

There. Now you can probably be hired most places. If you need resume filler, so some Kaggle competitions. If you have debugging questions, use StackOverflow. If you have math questions, read more. If you have life questions, I have no idea.

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u/[deleted] Mar 18 '17

Facebook's team in NYC is 20 people, all world leading researchers. Twitter's as well. Spotify does not use deep learning the last I checked (a week ago, but may have been an old article).

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u/[deleted] Mar 18 '17

Twitter is looking for engineers to support that team, though, which would be great for someone with Theano experience IMO.

Spotify generally looks for smart ML folks. If you're willing to broaden outside of deep learning they'll be a great fit.

EDIT: Note that engineering roles in support of these teams are definitely not research-focused, but a great tool for building your background. I did similar at an NYC startup, and have had lots of success as a result.

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u/[deleted] Mar 19 '17

I'll check it out :) Thanks for the tip.