machine-learning-optimization

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.

Workload-aware ML framework for PySpark performance prediction and configuration recommendation, validated through real benchmark execution and out-of-distribution evaluation.

GOFAI Propositional logic SAT solver for deterministic/stochastic expressions using Neo4J graph database

PyWave

Adaptation of a Chambolle scheme with isotropic & anisotropic terms for image denoising.