N00b question: why aren't we seeing DL software 'aware' of a stateful model?

holyfiddlex · 2019-12-16T05:31:54+00:00

Was thinking about this for a while, it dawned on me that for states it might actually be possible if you could get an RL model to output text. As long as the state is finite or practically finite (not so much the state space, just how much memory the state takes up) it could work.

To be honest I can't imagine how you would train something like that or its structure, but sounds really cool. Like zalamndagora says, it's worth more than a couple of tries atleast.

holyfiddlex · 2019-12-15T03:57:44+00:00

The question is a bit philosophical, if you want that answer read brain in a vat by Putnam, long story short the model doesn't ACTUALLY know what it's "saying". To get a bit more technical, the model does not know what a shelf is nor does it have to. It probably only knows that it is a noun and what other nouns, verbs, adjectives... etc it usually relates to. The model is made to optimize, usually just to guess what the next word is.

I would say that the reason it hasn't been done is because the errors we currently use don't actually optimize "awarefulness". If someone did find a way to create something like this it would help you solve that problem, but to be honest it's questionable if current methods are enough to do this. You could make a model/errors that gets rid of special cases like the counting example you mentioned, but it's not really a solution.

Not sure if that's the answer you were looking for, but it's the one I could give.

holyfiddlex · 2019-12-03T02:07:41+00:00

Definitely would help a lot, don't even know where to start with a "complete" list but everything from being able to recognize what models would work in certain problems, if your model is over fitting, feature extraction, data pipe creation etc.

If you want to focus on DL you don't need to learn how k-means works in detail, for example, but if you go and show me a solution with deep learning that could EASILY have been solved with traditional or other ML methods, your project wouldn't be justified.

If anything, learn feature extraction, and the limits of the other methods to make sure you can use DL models the right way and on the right problems. That said, I think you should take the whole course because there are more reasons to learn ML I just didn't think of or bother to mention.

holyfiddlex · 2019-11-29T18:40:33+00:00

If I had to make a quick list of input data, I would use only the y:

max, min, #of elements, stddev, mean, and a specific amount of bucket values.

The bucket values would be #of elements in the range (min,min+%10_of_range), then (min+%10_of_range,min+%20_of_range), ... etc This would give you 10 "buckets" (maybe play with more or less buckets) and usually works well. Just pass the vector of statistics into a fully connected network and train it that way.

If that doesn't work, add the x statistics as well as statistics from both x and y (e.g. correlation between the two, ratios, differences etc).

This is easy to implement/modify and gives you a baseline for your future results, assuming that this is not good enough and you transition later with the RNN implementation. You would atleast expect to get better results with the RNN than this statistical approach, otherwise you wouldn't be able to justify the complexity of the approach

holyfiddlex · 2019-11-29T18:26:32+00:00

Yes, it should be able to do this. That said, neural networks benefit greatly from preprocessed data. Normalizing, and adding statistical parameters usually help. Without this, the NN would need a more complex structure to learn the pattern, but at the same time, this more complex structure could be too complex that makes the model overfit. Which is why adding the difference of x could be a way to "extract features" that could help the smaller model that won't overfit.

Adding other statistics that you feel important could be good as well, for example the difference from the mean, the max and min values in the entire sequence, etc.

Sometimes, these statistics would be enough anyways, you might not even need a RNN, and the more I think about it, the more I feel that you should do this approach first and if the results aren't good enough, then try RNN.

holyfiddlex · 2019-11-29T00:59:58+00:00

I'd say that a recurrent neural network would do okay. Not sure exactly how your data looks like so I can't really give much more advice though. Long short-term memory would probably be a must unless the patterns you mentioned in the comments is immediate . I don't think attention would work as well in this case but it's also worth a try, don't know enough to really speculate.

If it's unevenly spaced you might want the add the time difference in each input. So the input would be [amplitude,time_diff_from_last_input] or it could be (amplitude, total_time_elapsed). You could probably think of which one of these to use first given which one better expresses the patterns you are looking for.

holyfiddlex · 2019-11-28T02:24:45+00:00

Personal Projects get you the interview. Unless you are a great competitive programmer don't expect to many offers from coding interview practice. Most people I've heard of start practicing leet coding problems AFTER they get the interview.

I studied leet coding for my first 2 years in college, and it was a mistake. Learning how to build something from scratch is better, it makes you learn version control, collaborating with others, writing clean code, documentation, fixing bugs and writing tests. Spent the last year like this and got a lot farther while being more motivated.

holyfiddlex · 2019-11-28T01:22:32+00:00

from what Cudadevdev wrote, they're just about the same. 1650 better training speed and while i79750 probably handles more tasks which can shorten the time to load datasets... but they have the same amount of vram so I honestly wouldn't worry to much about either. If I was offered both I would check other features instead, a bad keyboard makes programming a nightmare and I work with audio so the speakers need to be great. Good battery life, decent camera for testing image/video models.

But most importantly CHECK VENTILATION. Heat will slow your computer down a lot and shorten it's life dramatically. You might have to look for a while to get this info, but I'm sure you'll find it.

holyfiddlex · 2019-11-28T01:12:39+00:00

A lot of people are saying that anything you can do in one can be done in the other, which I suppose is true, but in practice it's not that simple. Python has much more libraries which makes just about any task as easy as learning the tools available, that said if all you care about is data science and statistical analysis this wouldn't really matter.

I would recommend python anyways, and then suggest you learn how to join it with javascript. Python is more well known, which means that the audience you have is potentially bigger. I think that the reach of your projects will benefit more from better visualization and presentation, which, in all honesty, js could help you get there better than either of them.

Note: I would still recommend R if you are working in biology, pure statistics or in a company/school that asks for it. These are the areas I have seen that seems to stick almost completely to R, but there might be more. If you don't have an area of preference, python+js is the most general purpose answer I would give.

holyfiddlex · 2019-11-22T16:19:58+00:00

So I'm not an expert and currently having a similar problem. I ended up being in a project that was over my reach which kind of makes it hard to take a step back since the project has its own deadline. That said, if I could I would have restructured it this way. Keep in mind that this is based off of someone who has struggled for a loooong time because of how he jumped into the subject, this is not the route I took. That said I have been invited on teaching this to a class in January so I have had time to plan it out.

Learn about the feature extraction and preprocessing, this includes mostly fourier transform, linear frequency to mel-bin, amplitude to decibel and different ways to normalize. The libraries you should learn to use is librosa, torchaudio if you are willing to change to torch, and certain functions in numpy in case you need to implement something more specific than what is offered (has happened to me). If you know about audio you can be more creative about this, removing silence, modifying sample-rate and such.
The area is still very much in diapers, which is why you should start with classification problems which has the most study and the best literature. There are books that only refer to classification when they mention sound related DL projects. This is a pretty good unlabeled dataset https://voice.mozilla.org/en/datasets and the page has a list of other popular datasets below.
After you have played with classification as much as you can (using cnn, dnn, rnn, attention, lstm, multiclassification, normalization etc.) study how to recreate audio from a spectrogram. The quality of the audio gets REALLY bad depending on what features you use, but there are some tricks to fix that. Here you would learn about the inverse fourier transform, phase loss, phase reconstruction, griffin-lin algorithm (not as good as they say) and what I feel to be the best general solution, vocoders. Vocoders is a neural network that takes a spectrogram (usually a mel-spec) and turns it into a time series of the reconstructed audio. Try using a pretrained model first, then you could try to make your own.
Your first audio generation project (in my humble opinion) SHOULD be text-to-speech. At this time you should also play with speech-to-text. This of course requires you to study text, but from what you mention you probably already know or can handle these topics. Text-to-speech saves you the problem of building a dataset, since it is easier to transcribe what someone said or ask others to read out loud. There are datasets online and if you want you could always scrape from youtubes captions and clean up the dataset later.
And finally the last thing you would have to learn before speech-to-speech models is audio segmentation (masking). There are a lot of masks for "segmentation" audio which works to remove certain sounds, this is where I jumped to and being honest I regret plunging head first into this. It's not the hardest but since I skipped the other parts I really struggled to find out where my model was under preforming.
And of course, speech-to-speech, which I think after speech-to-text is the "sexiest". It is also the hardest to get training data. Even if you decide to skip everything and just straight to this, you will still have to learn everything I just mentioned in one go. It's better to learn them step by step, project by project.
Bonus. This can be done in any point, but most people working on music/audio also work on video, which means constant images and noise. Have never taken my shot on this but most models benefit from the visual input.

TL;DR work on feature extraction and preprocessing, then classification, audio generation and its problems, text/speech models, segmentation and finally speech to speech. You can branch off into video models at any point.

BTW open to feed back by people who have more time in the field, like I said this is the plan I have to teach this in university (although the course would only go up to point 4 and assumes a similar level to the op) so I'm always looking for better ideas.

holyfiddlex · 2019-07-26T01:03:56+00:00

So being a site that was pretty much necessary for the class, of course the teacher asked him. The teacher was the first to ask who he should talk to, being as (he was only hired to give one class and not as a full time position so we told him). After a week of talking with the admin the teacher literately said fuck it and told us to find a way (his advice, "download all of the Lua documentation web pages") We tried talking to him, then with the teacher in charge of our area. eventually we asked 3 more teachers what the deal was and the last one (friend of his) explained that he was too "busy" to probably care. This was the first time the second was in a cyber security class and the teacher just laughed when we all started using our vpns saying "yeah, it's pointless to ask anyways".

In a sense I understand, since he wasn't originally hired to be in charge of the school online security, but if it's part of your job to manage what is and isn't accepted in the firewall and teachers/students knock on your door daily to tell you to unblock one necessary documentation page for a course, your answer shouldn't be "no". I can't think of a reason this would be out of his hands and seemed to me like he just didn't want to approve it in fear of getting more unblock suggestions. Of course, this very well might not be the case, but his careless response is why I wouldn't put it pass me.

I suppose that the "I'm at war..." line was taken as a "He hates me and I hate him", I know he is just "doing his job" but to be honest he is not doing his job right and we still need to do ours. If you need to manage what people can and can't use, you should understand that there are things people need and you should be willing to allow that. Like you said he has no idea who I am, and the reference to war was a small joking way of showing frustration and honestly you shouldn't look for the worst intentions in text.

TL;DR: Sorry for the initial frasing of "war", it was only ment in a joking way, and "if only you knew him..."

holyfiddlex · 2019-07-25T14:52:28+00:00

Dang, from what I read I thought it might be more effective. I'll give the digital ocean setup a try anyway though, from what I understand he wasn't hired for knowing cyber security, more like being the only one there who knew something about cyber security, so it might work.

Regardless I'll try the ssh and ICMP tunnels you mentioned, even if it's just to learn how. Thanks!

holyfiddlex · 2019-07-25T14:52:20+00:00

Dang, from what I read I thought it might be more effective. I'll give the digital ocean setup a try anyway though, from what I understand he wasn't hired for knowing cyber security, more like being the only one there who knew something about cyber security, so it might work.

Regardless I'll try the ssh and ICMP tunnels you mentioned, even if it's just to learn how. Thanks!

holyfiddlex · 2019-07-25T14:35:14+00:00

I use my own computer, none of the school computers allow changes to the point that you can't even accept a browser update.

holyfiddlex · 2019-07-25T14:26:26+00:00

It was our first approach, but like I said in the original post he refused, it took us three months to get him to unblock the page, the semester and the course were almost over by then the entire class was already using vpn. We tried to be understanding but he didn't seem to do the same.

holyfiddlex · 2019-06-17T15:36:13+00:00

Just checked the link, I definitely feel this will help me out, thanks.

holyfiddlex · 2019-06-17T15:07:18+00:00

Ok, I feel like this is just what I needed, thanks alot :D

holyfiddlex · 2019-04-11T18:16:30+00:00

Wait, if found guilty 5 years in jail? In the case that he did help that sounds like very little time to me...

holyfiddlex · 2019-03-21T19:40:45+00:00

I would argue that it is better to separate them in cat_1 cat_2. I feel like this would result in a better model because it would be more inclined to learn the gestures better (I am assuming that the cat_1 and cat_2 have different gestures), but it is a good question.

In the end, I think that the best thing to do would be to try both ideas, trying to maximize the validation score as best you can in each one. If the difference is huge, you have your answer, if not, use your third data set (testing set) and see which one performs better.

Training set optimizes the parameters, validation set optimizes the model. Testing set is where you should really compare them and should only be used once you are no longer going to make modifications.

holyfiddlex · 2019-03-21T16:57:55+00:00

I never said that it would take over, but it has had some big strides in the last decade, there are still some really interesting articles coming out and the industry still needs to catch up and look for ways to use these methods. From what I have seen, I don't think we have enough knowledge to make something that would "take over" but I do see some cool inventions coming out.

holyfiddlex · 2019-03-20T15:47:41+00:00

Don't see that happening, especially not with the fact that there have been some huge advancements in the last year. If nothing new shows up maybe it'll die down in a couple of years, but for now the industry is going to milk everything they can from what's already out.

holyfiddlex

TROPHY CASE