Do very simple text-preprocessing (a.k.a dirty work) with PreNLP Package !

I’m working in NLP part, and implementing a package to do iterative but necessary works for NLP. I want to know what you want to implement on the issue. I’ll implement it on this package.

Here are some exmaples to preprocess text.

from prenlp.data.normalization import * >>> url_normalize('Visit this link for more details: https://github.com/', repl='[URL]') Visit this link for more details: [URL] >>> tag_normalize('Use HTML with the desired attributes: <img src="cat.jpg" height="100" />', repl='[TAG]') Use HTML with the desired attributes: [TAG] >>> emoji_normalize('Hello 🤩, I love you 💓 !', repl='[EMOJI]') Hello [EMOJI], I love you [EMOJI] ! >>> email_normalize('Contact me at lyeoni.g@gmail.com', repl='[EMAIL]') Contact me at [EMAIL] >>> tel_normalize('Call +82 10-1234-5678', repl='[TEL]') Call [TEL] 

LINK: https://github.com/lyeoni/prenlp

I’m a Ph.D. student studying RL. I’m graduating soon, joining a top company as a software engineer. I have never wanted to become a professor, but I liked doing research. I found RL very interesting and still have some ideas that I’d like to work on, but the recent trends in RL in academia discouraged me doing research in RL. I’ve felt this way for a while but the recent RL tutorial at NeurIPS reminded me of it again. Lots of the papers after 2014 introduced in the talk were from either Deepmind, UC Berkeley, or MSR (which makes sense because the speaker is from MSR). I get why the speaker included those papers because they were cited many times and have been frequently discussed in their respective communities. Although there are many good papers from those groups and many of them are certainly amazing researchers, I think there are still other good papers published in top conferences which deserve to be continuously discussed. Things like experimental domains, benchmarks, and specific fields (or research directions) could be selected with a bias because of such trends. I wonder if it has always been this way or if this is something new and makes other people frustrated too.

Tomorrow at NeurIPS, Yoshua Bengio will propose ways for deep learning to handle “reasoning, planning, capturing causality and obtaining systematic generalization.” He spoke to IEEE Spectrum on many of the same topics.

https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/yoshua-bengio-revered-architect-of-ai-has-some-ideas-about-what-to-build-next

The thing about Python is that it’s ugly, forces you to code in a certain way and, most important factor there, is slow as a snail compared to other languages, even scripting ones like Lua. Are there any non-Python deep learning frameworks around that are in active development? I mean, there’s Torch (which is Lua, so good) but from what I’ve looked it’s abandoned in favor of PyTorch which is Python (bleh).

Preferably something either for Lua or .NET/Java – maybe not as fast as C/C++ would be but faster than python (blergh) and still allows for high productivity without worrying of random crashes because of some corrupted pointer.

Hi everyone! I’m doing my university final year project on a robot that deters foxes (and maybe cats 😉 ) from peoples gardens! The general plan is to use an artificial neural network to detect foxes from a camera feed. I’m guessing I need night vision data, because they tend to show at night… Anyone know where i can get training data from? Would splitting down a video into frames work as training data? (probably a stupid question, but how would i even do this!)

The initial idea was to have this running on a raspberry pi and i’m not sure if this would this even be possible because of the limited processing power?

Also I’d need a way of testing this! But i’m not allowed to use real animals (not that i could find and use a fox anyways!), Would toy stuffed animals work? But this i’m guessing would not work if I use night vision images in training my model.. I’m not sure

Does anyone have any experience in real time computer vision and advice on how to proceed!

Any help would be appreciated 🙂

If you are submitting machine learning preprints to arXiv, we now have a helpful categorization guide that we’d appreciate you read: https://blogs.cornell.edu/arxiv/2019/12/05/arxiv-machine-learning-classification-guide/ before submitting your next preprint.

We often see up to 250 new ML papers each day, and choosing the right category upfront really helps us with keeping the moderation process manageable!

Please let me know if you have any questions about the moderation process.