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A standard framework for modelling Deep Learning Models for tabular data
A Python-embedded modeling language for convex optimization problems.
A principled library for tuning, training and evaluating tabular data synthesis on fidelity, privacy and utility.
Software for evaluating the quality of synthetic data compared with real data.
Python interactive dashboards for learning data science
Official Repo for the Efficient Python for Data Scientists Book. You can buy the book from here:
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
This repository contains implementations of several common deep neural network (DNN) architectures. The goal is to provide a comprehensive collection of well-documented and easy-to-use models for e…
The fastai book, published as Jupyter Notebooks
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
Synthetic data generators for tabular and time-series data
This repository contains the official implementation of "A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data" (under review). You can use this codebase to replicate our experiments a…
A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…
A novel approach for synthesizing tabular data using pretrained large language models
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
Chronos: Pretrained Models for Probabilistic Time Series Forecasting
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"
A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.