Brainsteam

Content tagged with "Machine Learning"

Painless Explainability for NLP/Text Models with LIME and ELI5

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#machine-learning #work #explainability

Introduction

Explainability of machine learning models is a hot topic right now - particularly in deep learning where models are that bit harder to reason about and understand. These models are often called ‘black boxes’ because you put something in, you get something out and you don’t really know how that outcome was achieved. The ability to explain machine learning model’s decisions in terms of the features passed in is both useful from a debugging standpoint (identifying features with weird weights) and with legislation like GDPR’s Right to an Explanation it is becoming important in a commercial setting to be able to explain why models behave a certain way.

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Here be Dragons: Deep Learning Reproducibility

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#machine-learning #work #phd

A harrowing tale of trying to solve the impossible and failing. Episode 5 in this year’s run at the #100DaysToOffload challenge. See the full series here

Photo by Tim Mossholder from Pexels

Photo by Tim Mossholder from Pexels

That’s So Random: Randomness in Machine Learning

Training Machine Learning and in particular Deep Learning models generally involves a lot of random number generation. If we’re training a supervised classifier or regressor, we tend to randomly split our annotated data training set from our test set. Also, if you are training a new neural network it is fairly standard practice to randomly initialize the connections between the neurons (the weights) with a random number (here’s why).

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