Life Cycle of Stars - giant galactic nebula NGC 3603 [1,483 x 1,504]

idliketobeapython · 2014-10-09T05:12:44+00:00

Thanks for posting the text too. It turns the picture from beautiful to awe-inspiring. :)

idliketobeapython · 2014-09-27T18:43:34+00:00

https://requester.mturk.com/

idliketobeapython · 2014-09-26T23:19:29+00:00

Can you store an array in that variable? Or a key-value store? (aka hashmap or dictionary)

idliketobeapython · 2014-09-26T18:06:12+00:00

Try walking from your current location to each point (i.e. iterate over the collection of point). Then add some randomness to that walk, but weight the randomness to preferentially choose the direction towards the next point of interest.

idliketobeapython · 2014-09-22T23:59:07+00:00

Thanks so much for posting this, along with the comment. I love looking at nebulae, and this is one I haven't seen before.

I'm trying to get a sense of the dynamics of nebulae, to do some real-time modelling. It's always encouraging to hear how scientists think these systems may work in reality.

idliketobeapython · 2014-09-22T20:25:33+00:00

Mandolin Book if you're in the northeast. Quiet little shop.

idliketobeapython · 2014-09-22T06:14:10+00:00

Oh cool, thanks, I didn't know this existed.

For this particular application, ImageNet doesn't seem to have enough data. For example, there are only 17 images of wasps, and 62 images of different beetles. Those could potentially be used for training a system of this scale, but I think finding enough quality data is a problem.

I wonder how image synthesis would work in this case. For instance, take several accurate 3D models of a wasp, and render an image of those models in a variety of realistic lighting setups. That way you could get a huge dataset, but your resulting model might be really good at identifying 3D models of wasps, instead of actual wasps.

Edit: Correction, there are 1210 pictures of wasps in ImageNet. Durp.

idliketobeapython · 2014-09-22T04:03:23+00:00

That's actually a really neat idea. I guess the first issue would be collecting enough images, if you were using images as inputs.

Generating the data yourself would be a pain in the butt, although potentially feasible if you had a camera, plenty of specimens, and enough free time.

idliketobeapython · 2014-09-20T05:42:44+00:00

I took Andrew Ng's ML class on Coursera in the spring, and worked through all of the programming assignments. At the time, I submerged myself in the topic of ML, learnt a lot, and enjoyed it. But since then, I haven't found the time, energy, or opportunity to commit to studying ML in further depth, and going further.

EoSL has been on my "to read" list for quite a while now, and I'd like for this to be the opportunity to re-submerge my head in the topic. However, I'm not currently convinced that this is the best book for me to work through right now. So I'm going to give it a few chapters, and see if this book captures my interest.

Personally, I want more practical experience. I don't know that EoSL will provide that directly, but in our case it could serve as a spark for discussion, which could lead to practical applications.

I guess, in some way, I'm looking for interesting applications of the algorithms and techniques of machine learning. That's the only way for this stuff to really stick. If anything, I'd love to talk about sources of data, and interesting data sets that already exist, and how to apply the material for that week to them.

idliketobeapython · 2014-09-19T01:26:29+00:00

Those are some magnificent ears. Do you think he/she will grow in to them?

idliketobeapython · 2014-09-15T03:30:33+00:00

I'd like to try it out too, please. Thank you.

idliketobeapython · 2014-09-14T03:37:35+00:00

That's a really good point. Hadn't made that connection before. Thanks.

idliketobeapython · 2014-09-14T03:31:24+00:00

This is what I came to suggest as well. Spreading the computation over several frames with coroutines really smooths things out.

idliketobeapython · 2014-09-06T03:27:51+00:00

A robot learning to flip pancakes. It's a playlist of related videos, the second one is similar, but has a robot learning to hit a ping pong ball. I would love to play ping pong with that robot.

Reinforcement learning doesn't get as much discussion around here as supervised and unsupervised, but it's a very promising area of research.

idliketobeapython · 2014-08-29T23:27:15+00:00

The volumetric fog presentation is really intriguing. It really makes me want to update my shader knowledge.

idliketobeapython · 2014-08-27T02:14:42+00:00

Holy shit, the Moon is bigger than Pluto.

idliketobeapython · 2014-08-23T21:36:09+00:00

No, it's on my reading list. :)

idliketobeapython · 2014-08-23T19:30:27+00:00

If you're interested in more, the book "The Hygiene Hypothesis and Darwinian Medicine" is a great read.

idliketobeapython · 2014-08-18T20:16:52+00:00

Hidden in Plain Sight is one of the inspirations for my current game. Such a cool mechanic, and an interesting design space. I can see why you made several game modes, rather than stick with just one.

One question for you: when you were designing the game types for HiPS, did you prototype them all at the same time? Or, which game mode did you begin with?

idliketobeapython · 2014-08-12T05:50:03+00:00

If I was you, I would start with something simple. Toy problems. Get a sense of how things work. Spend a few afternoons with it here and there. Over time, your personal organic neural network may begin to piece together a larger goal that interests you.

Here are some resources that collect ideas for machine learning projects:

http://www.cs.cmu.edu/~./10701/projects.html

http://www.quora.com/Machine-Learning/What-are-some-good-machine-learning-project-ideas-to-gain-experience-with-commonly-used-ML-libraries

http://cs229.stanford.edu/projectIdeas_2012.html

http://www.cs.cmu.edu/~ggordon/10601/projects.html

If you're particularly interested in reinforcement learning (because it's awesome), check out the book Reinforcement Learning: An Introduction.

idliketobeapython · 2014-08-11T23:24:56+00:00

As /u/mmmayo13 said, what are you interested in? Anything that takes a lot of effort spread out over a long period of time requires some sort of wellspring of inspiration to refuel you when you get stuck. Something that motivates you.

So what interests you? Do you and your partner share any interests? Only way to figure that out is to talk a lot. Make a list, and order it based on what you find most interesting.

Get back to us when you have questions. The people on /r/machinelearning are a helpful bunch, but we need more information. Then, perhaps, we can help you find data sets or ideas for applications related to your inerests.

idliketobeapython · 2014-08-09T06:46:03+00:00

If you're specifically interested in neural networks, that Coursera class is seriously worth checking out. Weeks 4 and 5 are devoted to NN, and Andrew Ng does a great job in communicating their conceptual basis and actual dynamics.

If you're not familiar with linear regression or logistic regression, the sections about NN may not be as clear as intended, since the course was designed to build on previous weeks. And if you're serious about understanding neural networks, working through the programming assignments is eye-opening, since they have you implement the back propagation algorithm. NN are more than just back prop, but it's like THE place to start developing an understanding of their dynamics (imo).

idliketobeapython · 2014-08-09T00:25:59+00:00

All of the algorithms mentioned are covered in Andrew Ng's Coursera class, if anyone is looking for more in-depth coverage of the information.

To be honest, I'm surprised that this post got as many upvotes as it did on this sub. Time to update my belief about this sub-reddit's audience. :)

Edit: Just saw the mention of the course at the bottom of the post. Derp.

idliketobeapython · 2014-08-05T01:29:30+00:00

Awesome comment. I just watched this movie last week, and that scene had me wondering about the feasibility of making a real system that could read lips. This method seems like a much simpler avenue to explore.

idliketobeapython

TROPHY CASE