Sequential model-based optimization with a `scipy.optimize` interface

Example code for paper "Bilevel Optimization: Nonasymptotic Analysis and Faster Algorithms"

PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murray et al. 2019).

Flexible Bayesian Optimization in R

A small library for managing deep learning models, hyperparameters and datasets

Tools for Optuna, MLflow and the integration of both.

Hyperparameters-Optimization

Example Code for paper "Provably Faster Algorithms for Bilevel Optimization"

Hyperparameter optimisation utility for lightgbm and xgboost using hyperopt.

Some experiments to empirically analyze how the parameters of LWE impact the correctness of the algorithm on a single bit.

A scikit-learn compatible hyperparameter search that progressively eliminates low-scoring values — fewer fits, same best params.

A dl management front end

Hyper-Parameter Analyzer

Deep Learning Specialization. Master Deep Learning, and Break into AI

Study projects developed during data science courses

This repository Consist of Course Material, Assignment And Quizes Attempted in Specialization Course by Coursera

"oxayavongsa/projects" is a public GitHub repository serving as a diverse AI/ML Project Portfolio. Using Python coding and Juptyer notebook for multiple methodologies to model statistical algorithms.

A portable configuration tool to manage hyperparameters and settings of your experiments.

Interactive exploration of hyperparameter tuning results with ipywidget and plotly in jupyter notebook.

Automatically create a config of hyper-parameters from global variables