Digests » 120
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In this blog post, we present our recent advances in pretraining neural language models for biomedical NLP. We question the prevailing assumption that pretraining on general-domain text is necessary and useful for specialized domains such as biomedicine.
Straightforward implementations of interpretable ML models + demos of how to use various interpretability techniques. Code is optimized for readability.
This blog post explains the paper Hopfield Networks is All You Need and the corresponding new PyTorch Hopfield layer.
If you’re into data science you’re probably familiar with this workflow: you start a project by firing up a jupyter notebook, then begin writing your python code, running complex analyses, or even training a model. As the notebook file grows in size with all the functions, the classes, the plots, and the logs, you find yourself with an enormous blob of monolithic code sitting up in one place in front of you.
Random Forests are a widely used Machine Learning technique for both regression and classification. In this video, we show you how decision trees can be ensembled to create powerful predictive models.