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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

Tutorials and training material for the H2O Machine Learning Platform

Materials for GWU DNSC 6279 and DNSC 6290.

Comparison tools

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-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Talks & Workshops by the CODAIT team

Collection of presentations for advanced Python topics

Course repo for Applied Natural Language Processing (Spring 2019)

IPython notebooks on Gilbert Strang's MIT course on linear algebra (18.06)

Собрание видеозаписей докладов про JavaScript | 2018

《TensorFlow 快速入门与实战》和《TensorFlow 2 项目进阶实战》课程代码与课件

skimage-tutorials: a collection of tutorials for the scikit-image package.

An example of doing MovieLens recommendations using triplet loss in Keras

Code and models of paper " ECO: Efficient Convolutional Network for Online Video Understanding", ECCV 2018

Train a state-of-the-art yolov3 object detector from scratch!

VERIS Community Database

Machine Learning with Text in scikit-learn

Faster R-CNN for Open Images Dataset by Keras