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A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
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 ;)
A game theoretic approach to explain the output of any machine learning model.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
A scikit-learn compatible neural network library that wraps PyTorch
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Tutorial on scikit-learn and IPython for parallel machine learning
Code for the Lovász-Softmax loss (CVPR 2018)
A Toolbox for Adversarial Robustness Research
A smaller subset of 10 easily classified classes from Imagenet, and a little more French
An Open Framework for Federated Learning.
PyHessian is a Pytorch library for second-order based analysis and training of Neural Networks
TernausNetV2: Fully Convolutional Network for Instance Segmentation
Pytorch implementation of Augmented Neural ODEs 🌻
Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"
Solution for EEG Classification via Multiscale Convolutional Net coded for NeuroHack at Yandex.
3D augmentation and transforms of 2D/3D sparse data, such as 3D triangle meshes or point clouds in Euclidean space. Extension of the Fast.ai library to train Sub-manifold Sparse Convolution Networks
pyebm - A toolbox for Event Based Models
Perform image and time series classification of various cardiovascular conditions as well as COVID-19 using Electrocardiogram data.
Effective uncertainty estimation with decorellation and DPP mask for dropout