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.
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."
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.
It's in a very early stage (not production ready), but some colleagues and I have been working on a automated testing DSL built on top of RSpec that lets you generate API documentation from tests which validate the documentation is correct. It requires RSpec and a Rack-compatible service (incl. Rails). I think the idea has legs.
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?
There's a lot of room to grow here. Mostly the choices in channels are limited. I think that if people could write and share channels through some API, a ton of use cases could pop up.
This reminds me of Yahoo Pipes, but with webhooks instead of pubsub. Although, it seems they are using polling instead of webhooks.
You are right about query languages being a good for for monad comprehensions. See Torston Grust's dissertation[1], specifically "Combinators from Monad Comprehensions."
Last week I spent 17.5 hours in MacVim, 7 hours in a shell, and 2 hours on Github. That's out of 42 total recorded hours. On a related note, I love RescueTime.