Implementation of Stochastic Gradient MCMC algorithms
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Updated
Dec 7, 2016 - Python
Implementation of Stochastic Gradient MCMC algorithms
Bayesian hyperparameters optimization for neural networks
Preference Learning with Gaussian Processes and Bayesian Optimization
Simple Python library for bayesian optimization
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Experimental Global Optimization Algorithm
Final Project about optimizing NN with Bayesian Optimization for Bayesian Methods in ML course in Washington University
Northeastern Intro to Machine Learning take-home midterm 1
Barebones Python implementations of machine learning models, without using machine learning libraries
This project is for understanding and quantifying the errors in a machine learning or data analytic pipeline. Two approaches are explored. The first is using freezing and unfreezing of pipeline components (using optimization techniques like grid-search, random-search, Bayesian Optimization, Genetic Algorithms etc.). The second is using a gradien…
Bayesian optimisation for global black box function optimisation
Python 3.7 version of David Barber's MATLAB BRMLtoolbox
Python library for Bayesian hyper-parameters optimization
Multi-objective Bayesian optimisation framework.
Can machines identify genres? CS 419, IIT Bombay 2018
Gaussian Process approximator in Bayesian inverse problems (MCMC)
Accelerated Design of Layered Materials with Bayesian Optimization
Bayesian Optimization and Grid Search for xgboost/lightgbm
This repository provides commonly used modules from feature engineering to model training for machine learning tasks and kaggle competition.
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