Starred repositories
A playbook for systematically maximizing the performance of deep learning models.
Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.
An R package for Bayesian Marginal Structural Models
Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
Speed up model training by fixing data loading.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
📊 Path to a free self-taught education in Data Science!
Versatile End-to-End Recommender System
Retrieve author and publication information from Google Scholar in a friendly, Pythonic way without having to worry about CAPTCHAs!
The missing layers package for Bayesian inference
🔥 The Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data
InterpolateR is a high-performance R package for spatial interpolation, designed to provide fast, scalable and customizable solutions for grid- or point-based environmental data. Its simplified fun…
Code and data for the Transformer neural network trained to translate between molecular text representations and create molecular embeddings.
VIP cheatsheets for Stanford's CS 229 Machine Learning
Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
Simple, lightweight package for genetic algorithms on molecules
generic tools for causal inference with panel data
Code repository of the paper "Variational Stochastic Gradient Descent for Deep Neural Networks" published at
Python packaging and dependency management made easy
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques