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This is a repo with links to everything you'd ever want to learn about data engineering
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 ;)
Neural Networks: Zero to Hero
Companion webpage to the book "Mathematics For Machine Learning"
Probabilistic reasoning and statistical analysis in TensorFlow
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Algorithms for outlier, adversarial and drift detection
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalAI
A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
DeepSurv is a deep learning approach to survival analysis.
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series
Multiple Imputation with LightGBM in Python
TimeSHAP explains Recurrent Neural Network predictions.
A list of papers / videos / tutorials / blog posts on machine learning
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Code and data for the Transformer neural network trained to translate between molecular text representations and create molecular embeddings.
Tutorial on survival analysis using TensorFlow.
Code repository of the paper "Variational Stochastic Gradient Descent for Deep Neural Networks" published at
A model-agnostic framework for explaining time-series classifiers using Shapley values
Causal Inference using Deep Bayesian Dynamic Survival Models