lmombaerts Goto Github PK

amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

circadianbarley

Code associated to the inference of the circadian regulatory network of barley

dl_teaching

domain_adversarial_learning

Implementations of Domain Adversarial Learning algorithms

dyde

Code associated with "Dynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator". 2019

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

kaggle_volatility

Optiver Realized Volatility Prediction : https://www.kaggle.com/c/optiver-realized-volatility-prediction

ml_teaching

Various teaching scripts

multifactorial

Code associated with the publication "A multifactorial evaluation framework for gene regulatory network reconstruction". 2019

notebook-to-microservice

raman_gbm_lymphoma