Showing posts with label machine learning. Show all posts
Showing posts with label machine learning. Show all posts

Sunday, December 10, 2017

NIPS 2017

NIPS (Conference on Neural Information Processing Systems) is a top conference in machine learning.  I visited NIPS 2016 in Barcelona and it was a really awesome experience. This year it was in the USA (4/12 - 9/12).
The demand for the machine learning conference growths dramatically. Here is a funny picture of
Alex Lebrun‏@lxbrun with a title "NIPS conference registrations, 2002 through 2017":


This year conference had 6000 attendees. Visitors need to stay in the registration queue for approx. 2 hours. But, maybe,  thanks to that we have the ability to see the video from NIPS on the official NIPS FB page: https://www.facebook.com/nipsfoundation/.

I was really impressed by Peter Abeel keynote:
You can find more video here:  https://www.facebook.com/nipsfoundation/  and NIPS papers here: https://papers.nips.cc/
Also, here is a github repo with a lot of materials:  https://github.com/hindupuravinash/nips2017/

Wednesday, November 29, 2017

Model results explanation

Explaining the prediction of your model is a really crucial thing. Previously, it was a trade-off between accuracy and interpretability
But, now you can use LIME, explanation technique proposed by Ribeiro and al. at 2016. This technique learns interpretable model around the prediction. More details are in the original paper: https://arxiv.org/abs/1602.04938.



Monday, May 11, 2015

Emoji natural language processing

In Instagram Engineering Blog you can read about NLP techniques for discovering "context" of emoji
(like this one on the picture). They use word2vec for mapping every emoji to metric space and t-SNE as visualisation tool.
Full texts of articles:

Emojineering Part 1: Machine Learning for Emoji Trends

Emojineering Part 2: Implementing Hashtag Emoji
Emoji Wiki

Friday, January 9, 2015

Dive into Deep Learning

If you are interested in deep learning - you can try UFLDL (Unsupervised Feature Learning and Deep Learning) tutorial from Stanford University.
If this topic is really new for you - it is better to start from https://www.coursera.org/course/ml. After this session course will change format into self-study.