samg_

Hey, I agree with a lot of what you are saying here, but it seems unfair to cherry-pick a single method like you are doing here. There's a lot of confusing JavaScript out there, too.

In case this comment dissuades others from checking out Dr. Koller's class, I will add that I found her class challenging and well-constructed. I recommend it.

Google's Closure Compiler

I've taken Andrew Ng's Machine Learning class, Daphne Koller's Probabilistic Graphical Models class, Dan Jurafsky and Christopher Manning's Natural Language Processing class, and currently Geoff Hinton's Neural Networks class.

I have spent a lot of time on Khan Academy to learn the calculus. In my experience you can get by with a surprisingly small amount of calculus, but it happens to be a small amount from a high level.

For example, backpropagation is just repeated application of the chain rule. Did take a while to get a handle on the derivatives, but it's worth it.

A year ago, I would have no idea what this was about. I'm very thankful that I live in a world where I can take high quality classes from Coursera that have given me the foundation to at least understand the abstract. :)

If I have learned anything from ml-class, pgm-class, nlp-class, and now neural-nets, is that becoming a data scientist is one of the hardest things I'll ever eventually succeed in doing.

Yeah, I did a few trial runs on the cluster gpu instances maybe 4 months ago. I found that, while the gpus themselves were really quite fast, moving data in and out of gpu was not. Maybe amazon will focus on increasing bandwidth to the gpu for the new boxes.

"The numbers are, of course, averages. So parents may spend much more time, say, caring for children, while people without children will spend no time at all."

Stuffed at the bottom, of course. Averages lie!

I don't think that would be a classifier, or at least not reasonably. You could have "In Soviet Russie X Y you" for each X,Y as your classes, but that would be unreasonable.

Yakov Smirnoff is a structural joke. You would need to parse sentences, pattern match, transform it, and then do some kind of regression on the phrase to get its humor quotient.

The Stanford Parser for structural parsing, then some custom pattern matching and transforming code, might get you somewhere.

Just upgrading to rspec 2.8 made my test suite run 10x faster. You might have David to thank for a lot of that speed up. :)

For me, one of the biggest obstacles to understanding lots of Haskell code is opaque infix operators.

It would require an Internet connection, but GPS + some weather API would do.

I am just learning some of these machine learning tools and am rapt, so forgive me for asking, but would you be able to explain a little about what you are doing?

How are you generating features? Stanford parser? Are you using logistic regression or something more advanced?

I love the idea. I am interested in applying some of these concepts myself. Do you have any ideas that you are not able to pursue yourself, that I might take a crack at?

I would happily use G+ as my blog for programming related things, but I would need to be able to make posts public and not spam my non-technical circles. Is that possible?