Stars
Typer, build great CLIs. Easy to code. Based on Python type hints.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
This is the preprocessing step of the LIDC-IDRI dataset
A unified framework for machine learning with time series
code for Data Science From Scratch book
Algorithms for explaining machine learning models
Quant/Algorithm trading resources with an emphasis on Machine Learning
Master the command line, in one page
Visualizer for neural network, deep learning and machine learning models
Source code of The Neural Hawkes Process (NIPS 2017)
Module for statistical learning, with a particular emphasis on time-dependent modelling
A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)
This is a template repository to start using LaTeX, with TU Darmstadt corporate design installed, in Docker
The most cited deep learning papers
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Text and supporting code for Think Stats, 2nd Edition
A curated list of awesome Go frameworks, libraries and software
A high performance HTTP request router that scales well
A curated list of awesome Deep Learning tutorials, projects and communities.
Security Guide for Developers
⚡ Delightful Node.js packages and resources