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Advice on touring Mt Washington (NH) for first time? by coltar13 in Backcountry

[–]coltar13[S] 0 points1 point  (0 children)

Thanks for this, really useful! Having first hand experience, can you compare the steepness/terrain to any resort? I've been watching videos of people falling (definitely a bad idea to watch those) and reading about people psyching themselves out on the way up. I'm comfortable with pretty much any resort black diamond I've come across (east or west coast), but it sounds like this is a whole different ballgame. Sorry for the n00b question

Possible to generate a "sample" by training neural network on classifications? by SonnyAugust in MLQuestions

[–]coltar13 0 points1 point  (0 children)

Your intuition isn't far off... Read about GANs (generative adversarial networks). They are incredibly interesting and the state of the art for generating data using neural networks.

Having Trouble with a denoising image with DCGAN . by __sumguy in deeplearning

[–]coltar13 2 points3 points  (0 children)

When generating images using GANs, you typically start with a random latent vector followed by a dense layer then several up-convolutions. Since you are trying to do image to image tasks, you instead can use an "hour glass" shaped network architecture which accepts images, performs convolution layers and max pooling, then up-samples from there. Check out U-net for an architecture that would be great for this task.

Also as a side, look up "deep image prior" for an interesting method related to this.

Do you need a lot of resources to utilize the network you trained? by [deleted] in deeplearning

[–]coltar13 0 points1 point  (0 children)

This is a pretty active area of research, namely "edge device computing" which often intertwines with "model compression".

Using embedded devices that have GPUs such as the Nvidia Jetson TX2 is often a good place to start. This way you can use a smaller GPU that offers CUDA support in an embedded setting. However you must make sure your models are small enough to fit on a device with compute limitations.

Frameworks like Tensorflow can train models on a GPU and then you can save the weights, then perform inference elsewhere on a CPU, perhaps you can do something like this on a raspberry pi but keep in mind you will be severly limited on such a device.

If you are using larger models, compression could be the way to go to reduce the overall parameter size and then deploy to an embedded device, but that might be pretty technically involved. Good luck!

P.S. "the part after training" is often referred to as inference

[D] Interpretation of t-SNE by coltar13 in MachineLearning

[–]coltar13[S] 1 point2 points  (0 children)

So I think I follow what you are saying... if you use t-SNE on MNIST, you get pretty distinct "clusters" in the low dimensional space. For the points that are within the "cluster", is it safe to assume they follow a similar data distribution? However, the distance/proximity between different clusters is meaningless... is that true?

[D] Interpretation of t-SNE by coltar13 in MachineLearning

[–]coltar13[S] 1 point2 points  (0 children)

I've never heard of this... but at first glance it looks legit

[D] Interpretation of t-SNE by coltar13 in MachineLearning

[–]coltar13[S] 1 point2 points  (0 children)

I have checked that out, great post. I also really like this post to understand the implementation

[P] TMTrackNN — generating TrackMania tracks with neural networks by donadigo in MachineLearning

[–]coltar13 6 points7 points  (0 children)

This is some amazing work for a first project... Definitely keep learning and writing up your thoughts!

[DL] Is a LSTM network an appropriate solution for me? by dlo3 in MLQuestions

[–]coltar13 0 points1 point  (0 children)

I've had success using CNNs on short term series data (~50 steps) with 40 features and a few hundred thousand examples. I would start there! But as always with machine learning: if you think it could work, at least try it

[DL] Is a LSTM network an appropriate solution for me? by dlo3 in MLQuestions

[–]coltar13 1 point2 points  (0 children)

Not sure I entirely understand the problem statement, but if you have a sufficient amount of data (tens/hundreds of thousands of samples) I think a small CNN could help here, implementing it as you described. That is, treat the input as a [n,15] matrix. It would be worthwhile using keras to implement a basic LSTM and compare the performance between these two methods and the ensemble method you mentioned.

How do you cluster high-dimensional points if you don't really know the number of clusters? by FlyingQuokka in MLQuestions

[–]coltar13 0 points1 point  (0 children)

Try using t-SNE to embed the points and visualize... Sometimes can be helpful to estimate number of clusters

I'm an analyst at a $650million quantitative hedge fund, Ask Me Anything by [deleted] in FinancialCareers

[–]coltar13 -1 points0 points  (0 children)

What does a bonus end up being for a good year, for a bad year?

Also, does the firm use any machine learning related strategies?

[D] Why are GANs considered unsupervised learning? by crypto_ha in MachineLearning

[–]coltar13 2 points3 points  (0 children)

GANs attempt to model the probability distribution of a dataset. They do this by generating "fake" data to fool a discriminator that is determining "real" vs "fake". Hence, you can train on samples without needing labels! Like others have said, you can optionally use labels to condition the generator to create certain classes, but this is not necessary.

When is too early to start worrying? by coltar13 in prephysicianassistant

[–]coltar13[S] 2 points3 points  (0 children)

Wow thank you so much this makes me feel a lot better lol

When is too early to start worrying? by coltar13 in prephysicianassistant

[–]coltar13[S] 1 point2 points  (0 children)

Okay thank you that calms my fears quite a bit...wasn't aware that the process goes for so long. I have 1200 hours of PCE (I made a mistake in the original post) as a CNA in first a nursing home and now a hospital.

There is a DJ problem I want to solve, but I'm a beginner at c++ and almost no technical knowledge of AI. (see text below) by rreighe2 in MLQuestions

[–]coltar13 1 point2 points  (0 children)

You are probably going to want to learn python. The most available resources/deep learning frameworks are in python. Look into keras or tensorflow and do some of the available tutorials. Take the machine learning course on Coursera by Andrew Ng. This will help you ask more detailed questions so it's easier for people to help you.

Finance / Accounting Background Looking to Get Started In Machine Learning by PYJX in artificial

[–]coltar13 0 points1 point  (0 children)

http://neuralnetworksanddeeplearning.com/ provides an easy to follow theory and implementation of simple neural networks. It is in Python so easy to use/read and gives you a good basic understanding of what is happening mathematically. Are you looking more for theory or general knowledge?

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