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Text Featurization In Machine Learning

Photo by Glen Carrie on Unsplash

In Machine Learning, it is nice when models are simple and interpretable, since it reduces the burden of debugging a complex model when things don’t work. But simple models do not always lead to the best results. Similarly, there are simple text featurizer which doesn’t result in effective models.


Self-Supervised Approach To Learning

BYOL Paper

What is Self-Supervised Learning?

It is a method of machine learning where the model learns from the supervisory signal of the data unlike supervised learning where separate labels are specified for each observation. It is also known as Representation Learning.

Note, the model’s learned representation is used for downstream tasks like BERT, where language…

Dealing with Large Deep Models

Photo by Patrick Tomasso — Unsplash

Deploying memory intensive large deep models has a great downside if you’re planning to deploy the model in edge devices for real time inference or systems with memory constraints. Edge devices have limited memory, computing resources, and power that means a deep learning network must be optimized for embedded deployment.

Mayur Jain

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