scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.

The Startup CTO's Handbook, a book covering leadership, management and technical topics for leaders of software engineering teams

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

Portfolio optimization and back-testing.

Shapley Interactions and Shapley Values for Machine Learning

OCR & Document Extraction using vision models

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

📍 A map of my life, where each week I've been alive is a little box.

Fast Semantic Text Deduplication

Content Based Recommendation System TikTok-style with just raw video data. Gemini API and Embedding

Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3

Python library for Applied Computational Supply Chain & Logistics. Unlock Neural Nets, Bayesian EOQ, Optimization, Time Series, and more for smarter decisions.

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

A fast and flexible Python package for efficiently solving lasso, elastic net, group lasso, and group elastic net problems.

The book every data scientist needs on their desk.

nannyml: post-deployment data science in python

Shared Blogs and Notebooks

Notebooks for Applied Causal Inference Powered by ML and AI

World beating online covariance and portfolio construction.

Fast and modular sklearn replacement for generalized linear models

Examples for Cerebrium Serverless GPUs

This repository is the the implementation of the JAIR paper: https://doi.org/10.1613/jair.1.15320. This repository provides the codebase for benchmarking Predict-then-Optimize (PtO) problems using …

Download batas administrasi indonesia dalam format SHP, kml, geojson, dan geopackage (gpkg)

Minimalistic TensorFlow2+ deep metric/similarity learning library with loss functions, miners, and utils as embedding projector.

Additional linear models including instrumental variable and panel data models that are missing from statsmodels.

Generate embeddings from large-scale graph-structured data.

Hands-On Graph Neural Networks Using Python, published by Packt

Code for the Actuarial Data Science Tutorials published at https://actuarialdatascience.org.

Design-Based Inference Mixtape Session taught by Peter Hull