Putting ML in Production
A guide and case study on MLOps for software engineers, data scientists and product managers.
production mlops course tutorial
Normalization Techniques for Training Very Deep Neural Networks
How can we efficiently train very deep neural network architectures? What are the best in-layer normalization options? Read on and find out.
normalization batch-normalization layer-normalization group-normalization
How to Deploy your ML models as Telegram Bots
In this project, I trained a Model to detect mask on people's face and made it available on both Android and IOS through a Telegram Bot. It's deployed on ...
deep-learning fastai telegram-bot heroku
A 🤗 transformers-style implementation of BERT using LambdaNetworks instead of self-attention.
transformers bert lambdanetworks self-attention
Tldrstory: AI-powered Understanding of Headlines and Story Text
A framework for AI-powered understanding of headlines and text content related to stories.
zero-shot-learning text-similarity similarity-search streamlit
A Practical Guide to Graph Neural Networks
How do graph neural networks work, and where can they be applied?
graph-neural-networks survey arxiv:2010.05234 paper
Introduction to Reinforcement Learning
In this video I give a brief introduction to Reinforcement Learning.
reinforcement-learning code notebook video
A Visual Guide to Regular Expression
A mental model of how various components of a regular expression work from the bottom-up.
regex preprocessing text-matching python
Distribution Based Compositionality Assessment (DBCA)
A method of systematically generating datasets with train and test splits diverging in a controllable and measurable way.
semantic-composition knowledge-base question-answering semantic-parsing
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