Subject archive for "machine-learning-featured"

Data Science

Building a named entity recognition model using a BiLSTM-CRF network

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By Nikolay Manchev13 min read

Machine Learning

Powering Up Machine Learning with GPUs

Whether you are a machine learning enthusiast, or a ninja data scientist training models for all sorts of applications, you may have heard of the need to use graphical processing units (GPUs), to squeeze the best performance when training and scaling your models. This may be summarized by saying that training tasks based on small datasets that take a few minutes to complete on a CPU may take hours, days, or even weeks when moving to larger datasets if a GPU is not used. GPU acceleration is a topic we have previously addressed; see "Faster Deep Learning with GPUs and Theano".

By Dr J Rogel-Salazar14 min read

Data Science

Make Machine Learning Interpretability More Rigorous

This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for considering a rigorous evaluation of interpretability. Insights are drawn from Finale Doshi-Velez’s talk, “A Roadmap for the Rigorous Science of Interpretability” as well as the paper, “Towards a Rigorous Science of Interpretable Machine Learning”. The paper was co-authored by Finale Doshi-Velez and Been Kim. Finale Doshi-Velez is an assistant professor of computer science at Harvard Paulson School of Engineering and Been Kim is a research scientist at Google Brain.

By Ann Spencer8 min read

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