A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

(Deprecated) Scikit-learn integration package for Apache Spark

LAMA - automatic model creation framework

Automated modeling and machine learning framework FEDOT

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

A unified interface for optimization algorithms and experiments

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Hyperparameter optimization in Julia.

Workflow engine for exploration of simulation models using high throughput computing

:zap: Fast Concurrent / Parallel Sorting in Go

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Alchemy Cat —— 🔥Config System for SOTA

Purely functional genetic algorithms for multi-objective optimisation

Framework of intelligent optimization methods iOpt

An abstraction layer for parameter tuning

Globally Safe Model-free Exploration of Dynamical Systems

A Python Toolkit for Managing a Large Number of Experiments

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

A CLI-based tuner that runs multi-objective optimization on your ESPminer.

Learning simulation parameters from experimental data, from the micro to the macro, from laptops to clusters.

Trying PostgreSQL parameter tuning using machine learning.

Machine Learning Project using Kaggle dataset

Codes and templates for ML algorithms created, modified and optimized in Python and R.

Robustness of DWT vs DCT is graded based on the quality of extracted watermark. The measure used is the Correlation coefficient (0-100%).

MATLAB simulation of a BPSK data transmission system with AWGN channel, and its benchmark against BER(SNR).

Algorithm Configuration Visualizations for irace!

Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli. However, to achieve overall balanced/interesting behaviour the relative importance of these instincts, as well their internal parameters, must be tuned. In this project, you will learn how to apply Genetic Programming as means of such tuning, and attempt to achieve a series of non-trivial swarm-level behaviours.

Interactive image viewer and processing application for computer vision research and real-time parameter tuning with OpenCV

Automatic Tuning for Post-Quantum Cryptography on CUDA, based on MLWE problems.

The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.