We were toying with this simple setup to autocomplete python with deep learning

https://github.com/vpj/python_autocomplete

The metric we used to measure the performance was the number of keystrokes saved. That is the model gives a single suggestion of length L and L-1 keystrokes are saved if it matches actual code. It performs pretty well saving around 30-50% key strokes despite being naive.

Next we want to try different architectures and improve inference performance. Right now it’s too slow for any actual usage.

If you have any ideas or suggestions please do share.

Hi everyone, one of our writers wrote an article on FloydHub blog that aims to give a checklist which a machine learning practitioner can refer from time-to-time in his own projects. The article tries to combine the pieces of advice collected from practitioners like Andrej Karpathy, Josh Tobin and at the same time includes many experiences.

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Did we miss anything? Is there something you would like to see in depth in the next articles?

I hope you enjoy it!

Hi all,

None my previous deployments of a keras model have been time critical – waiting a few seconds for the python script to load in all the libraries (like tensorflow, numpy), load up the model, etc etc. was fine.

However, I’m now dealing with a situation where I’m going to need to query my deployed model every 1 second or so – and get a response back as quickly as possible. This means that my usual method of calling a python script via a .bat file, passing in an argument with my csv filename(using Process() in my .net app) and reading the stdout will not suffice.

I need a way to keep a python session alive somehow, with the libraries and model loaded into ram – waiting for some sort of call that points towards my csv file(s) that need to be queried against the model. Note that I must initiate this call [and get a response back into] my C# code, as this is part of a bigger system that relies on other C# libraries.

Anyone have thoughts about how to go about this?

https://www.goodreads.com/book/show/36722636-the-deep-learning-revolution

I am about a third of the way into this book and it has me feeling a little forgetful of where the community is going with DL.

The book is mostly a memoir of Sejnowski and the work he did with Hinton to help get the community to where it is today. There are chapters dedicated to things that I almost never see discussed anymore, such as how the brain actually functions as a general purpose problem solving machine. Paper after paper it seems like there is a novel tweak on a DL application, with sometimes exciting results, but I can’t remember the last time I was excited about what the community has produced since AlphaZero.

Are we entering a maturity phase in DL? Simply endlessly optimizing what we already know? Where is the new epicenter of revolutionary thought? Why is there so little talk of neuroscience/biology in the DL community despite the strong roots?

Disclosure: I am not an academic. I just like to read a lot about ML/DL, gene modification, quantum computing, and economics. Just the cool stuff.

So where do you guys think we are? Where are we headed?

I made a dataset of research paper section summaries; each datapoint contains a section of a research paper (intro, background, methods, results, discussion, conclusion, etc. ) and a corresponding summary.

The dataset contains ~4.3 million data points from ~11 million papers.

Unfortunately a lot of the files I host have gone down pretty often due to too many downloads, ever since my group was recently featured in a Siraj video (lol) . So we’re going to be doing a soft release for now, if you want a link please PM. We especially encourage those with previous experience with summarization and/or scientific texts to start playing around with it.

This has been circulating around the NLP/ML circles:

https://cs.nyu.edu/~kann/documents/relationshipsinnlp.pdf

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I won’t out “C” but most people in NLP know who he is (a fairly well-known junior faculty).

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Obviously she was treated horribly and the guy is an asshole. And she was incredibly brave to post this. However, I can’t help feel that this is beginning to verge on “personal” rather than professional. The one allegation that definitely warrants disciplinary action would be her claiming that the ex deliberately gave her paper a low review (this could easily be verified by the committee). But aside from this, it seems like more of a she-said he-said type of thing…

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Thoughts?

This article aims to distill some of the important concepts involved in using neural networks for super resolution. Emphasis was provided on setting up the loss functions and metrics while keeping the blog as “easy-to-follow” as possible. Link: https://medium.com/beyondminds/an-introduction-to-super-resolution-using-deep-learning-f60aff9a499d?source=friends_link&sk=f12ee4a026d08747468297ac56b8df29

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I would love to hear your thoughts and feedback regarding the same, thanks!

Hello

I made a visualization of dependencies for some methods of NeurIPS 2018. This graph helped me in my research, and at some moment I realised that it should become public. Now I am preparing it for release.

https://twitter.com/INFORNODEMO/status/1147332691999842305

Graph is built on dependencies between individual models and their components. Simple example: Generative Adversarial Nets (GAN) consist of Generator(GEN), Discriminator (DIS) and Adversarial Training (AT) method, and Adversarial Autoencoder (AAE) is based on DIS, AT and Autoencoder (AE). So for AAE we will have an edge to AE, AT and DIS.

I use it to get associations that can help create something new, and I hope that it will be useful not only for me.

More information coming soon.

Please share and retweet if you are interested in it: https://twitter.com/INFORNODEMO/status/1147332691999842305