News for hackers. Stuff that matters.
Re:The Unreasonable Effectiveness of Deep Feature Extraction by asavinov on Thursday February 14, 2019 @01:49PM (#19164657)
Deep feature extraction is important for not only image analysis but also in other areas where specialized tools might be useful such as listed below:

o https://github.com/Featuretools/featuretools - Automated feature engineering with main focus on relational structures and deep feature synthesis

o https://github.com/blue-yonder/tsfresh - Automatic extraction of relevant features from time series

o https://github.com/machinalis/featureforge - creating and testing machine learning features, with a scikit-learn compatible API

o https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last! The workflow engine allows for integrating feature training and data wrangling tasks with conventional ML

o https://github.com/xiaoganghan/awesome-feature-engineering - other resource related to feature engineering (video, audio, text)

Re:The Unreasonable Effectiveness of Deep Feature Extraction by mlucy on Thursday February 14, 2019 @01:54PM (#19164697)
Definitely. There's been a lot of exciting work recently for text in particular, like https://arxiv.org/pdf/1810.04805.pdf .
Re:The Unreasonable Effectiveness of Deep Feature Extraction by nl on Thursday February 14, 2019 @09:58PM (#19168127)
Or from today, OpenAI's response to BERT: https://blog.openai.com/better-language-models/

Breaks 70% accuracy on the Winograd schema for the first time! (a lazy 7% improvement in performance....)

Re:The Unreasonable Effectiveness of Deep Feature Extraction by psandersen on Friday February 15, 2019 @11:10AM (#19172011)
This is a great resource, thanks for sharing!

I'd be interested to hear what kind of experience people are having with these frameworks in production.