Stars
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K…
Book about interpretable machine learning
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Acceptance rates for the major AI conferences
Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Fast and Easy Infinite Neural Networks in Python
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
Koç University deep learning framework.
A Toolbox for Adversarial Robustness Research
Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
PyHessian is a Pytorch library for second-order based analysis and training of Neural Networks
DeepSurv is a deep learning approach to survival analysis.
Python package for dataset imports from UCI ML Repository
Understanding computation in artificial and biological recurrent networks through the lens of dynamical systems.
CardIO is a library for data science research of heart signals
Code for NeurIPS 2019 paper: "Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes"
A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch
Resource library for getting started with deep learning work using electrocardiograms
A toolkit for analysis, synthesis, and digitization of electrocardiogram images
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
Code for the paper: "Tensor Programs II: Neural Tangent Kernel for Any Architecture"
Official implementation of "Energy-Based Models for Deep Probabilistic Regression" (ECCV 2020) and "How to Train Your Energy-Based Model for Regression" (BMVC 2020).