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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:
by asavinov 8y ago
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 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 https://github.com/blue-yonder/tsfresh - Automatic extraction of relevant features from time series
o https://github.com/machinalis/featureforge https://github.com/machinalis/featureforge - creating and testing machine learning features, with a scikit-learn compatible API
o https://github.com/asavinov/lambdo 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 https://github.com/xiaoganghan/awesome-feature-engineering - other resource related to feature engineering (video, audio, text)
- mlucy 8y agoDefinitely. There's been a lot of exciting work recently for text in particular, like https://arxiv.org/pdf/1810.04805.pdf https://arxiv.org/pdf/1810.04805.pdf .
- nl 8y agoOr from today, OpenAI's response to BERT: https://blog.openai.com/better-language-models/ https://blog.openai.com/better-language-models/ Breaks 70% accuracy on the Winograd schema for the first time! (a lazy 7% improvement in performance....)
- psandersen 8y agoThis 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.