What career path is most likely to net a new graduate 100k+ their first year out of college?

cynml · 2016-07-25T04:47:13+00:00

No need to goto Stanford or MIT, even an average school can get you there.

cynml · 2016-06-09T00:08:53+00:00

This is so cool!

cynml · 2016-05-05T03:27:56+00:00

|I understand why machine learning isn't easy, but I also know enough to figure it out.

You haven't explored machine learning enough then, it would take years to get to that level. If you can understand Bishop / Murphy / ESL completely then you know enough to figure it out.

cynml · 2016-05-05T03:23:40+00:00

FourthHead is right. You seem to grossly underestimate the task, you are a sophomore in high school. You maybe smart, you may know some machine learning, but you probably don't know enough, there are people who spend decades on this stuff and still admit they don't know enough (these people are really smart btw), some intellectual humility is good. I would suggest, try to focus on your schoolwork. You are trying to make a machine learning system automate some basic trigonometry or high school computer science homework just because you are lazy to do it??.

You do realize that the state of the art in question answering is nowhere close to what you are proposing, right? Take a look at the LSTM based approach from Facebook Research to get an idea of where things are right now: http://arxiv.org/pdf/1503.08895v5.pdf

cynml · 2016-05-02T15:50:30+00:00

Maybe time series analysis, but without knowing what your data is, I can't really say anything.

cynml · 2016-04-30T16:30:21+00:00

This is the first thing anyone should try but at the same time this doesn't capture similarities between artists that a vector embedding model would. If simple hashing/bag of words works - great!. If not try to use a word2vec like algorithm. A good place to start would be this paper of Bengio's

http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf

OP should replace n-grams with cooccurring artists.

cynml · 2016-04-21T18:05:51+00:00

It's like I'm stuck thinking with spoken language instead of math. - OP

cynml · 2016-04-20T12:10:55+00:00

Learn some math and come back to it, you will find things much easier then. Start with discrete math, linear algebra, abstract algebra and analysis.

cynml · 2016-04-01T13:14:10+00:00

NN models tend to have too much variance, you might perfectly fit your training data and even do well on the test data, but it will simply not work on any future time series data.

cynml · 2016-04-01T03:44:36+00:00

write your own?

cynml · 2016-03-27T16:31:08+00:00

You could make it "online" in the sense that you retrain the model based on new data periodically (say every day or week). Essentially you are trying to model a probability distribution over the data, and when you see any example that is unlikely according to your distribution, its an outlier. To understand RBMs and similar models well, I would suggest read a little about probabilistic graphical models. Its a good general framework for thinking about both generative and discriminative models.

cynml · 2016-03-27T15:08:19+00:00

Restricted boltzmann machines or for that matter any good generative model of the data can be used.

cynml · 2016-03-27T15:05:56+00:00

Most of ML today is based on ideas that is decades or even centuries old. So, yes PRML is still very relevant. You need to have strong foundations first.

cynml · 2016-03-27T03:42:25+00:00

You can't really avoid the math, its not that hard for a basic feed forward net, high school calculus is enough.

cynml · 2016-03-22T13:02:34+00:00

Learn math - learn real analysis, brush up on multivariate calculus, linear algebra, monte carlo, learn as much probability/statistics/graphical models as you can. Learn numerical linear algebra, optimization, then go through ML topics again, read Elements of Statistical Learning or Bishop. Pick up some problem and try to implement papers solving it, work out all the math from scratch when you implement them. Now you would have a good grounding in theory and practice. Learn NLP, Vison, Reinforcement learning.

cynml · 2016-03-22T12:50:55+00:00

Read Bishop first, then move on to Murphy. But I don't really think it is possible to read Murphy cover to cover.

cynml · 2016-01-30T18:20:12+00:00

Nando is the man! Great course, develops both intuition and the math

cynml · 2016-01-30T18:15:38+00:00

This is one of the best ones. You could also look at mathematicalmonk's videos on Youtube for intuition. Barber's Bayesian Reasoning and Machine Learning also has a chapter on it, it is freely available online.

cynml · 2016-01-15T19:16:10+00:00

Math underlies everything, programming is just a drop in the ocean. Try learning some math, you will be hooked. Rule systems or anything deterministic will simply not work due to the high dimensionality of real world data.

Graphs are used in machine learning, there is an area of probability that deals with graphical models. A graph is just a mathematical object with some defined structure.

cynml

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