For more content like this, follow Insight and Emmanuel on Twitter. Text data is everywhere Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (
BERT, RoBERTa, DistilBERT, XLNet: Which one to use? Lately, varying improvements over BERT have been shown — and here I will contrast the main similarities and differences so you can choose which one to use in your research or application. By Suleiman Khan, Ph.D., Lead Artificial Intelligence Specialist Google’s BERT and recent transformer-based methods have taken the NLP landscape by a storm, out
CatBoost vs. Light GBM vs. XGBoost Who is going to win this war of predictions and on what cost? Let’s explore. By Alvira Swalin, University of San Francisco I recently participated in this Kaggle competition (WIDS Datathon by Stanford) where I was able to land up in Top 10 using various boosting algorithms. Since then, I have been very curious about the fine workings of each model including param
5 Great New Features in Latest Scikit-learn Release From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention. By Matthew Mayo, KDnuggets Managing Editor on December 10, 2019 in Data Preparation, Data Preprocessing, Ensemble Method
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