Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Curated list of recommnedation system topics

We write sample code for two tower models for retrieval and add RLHF/RLAIF style alignment with a ranking model to make the retrieval more aligned with the ranking model on top

This repository is intended to host tools and demos for ActivityNet

Python logging made (stupidly) simple

Examples for https://github.com/optuna/optuna

Part of my daily plan for studying Python.

Mini website for testing both general CS knowledge and enforce coding practice and common algorithm/data structure memorization.

Algorithms and data structures for review for coding interview.

Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.

Deepfakes Software For All

This repository contains the source code for the paper First Order Motion Model for Image Animation

Pytorch implementation of BERT4Rec and Netflix VAE.

🛍 A real-world e-commerce dataset for session-based recommender systems research.

How to do bash commands you always forget. OpenAI-powered.

Free MLOps course from DataTalks.Club

Best Practices on Recommendation Systems

A curated list of the top 10 computer vision papers in 2021 with video demos, articles, code and paper reference.

The Open Source Feature Store for AI/ML

🏅 Collection of Kaggle Solutions and Ideas 🏅

A unified, comprehensive and efficient recommendation library

Accelerated deep learning R&D

Deep Learning with Catalyst

This is a guide to PySpark code style presenting common situations and the associated best practices based on the most frequent recurring topics across the PySpark repos we've encountered.

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

Natural Language Processing with Deep Learning

Deep Learning for Biology course materials / Higher School of Economics, 2019