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alan-turing-institute / MLJ.jl

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LiYangHart / Hyperparameter-Optimization-of-Machine-Learning-Algorithms

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

machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperband
  • Updated Oct 27, 2020
  • Jupyter Notebook

ivan-zapreev / Distributed-Translation-Infrastructure

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The distributed statistical machine translation infrastructure consisting of load balancing, text pre/post-processing and translation services. Written in C++ 11 and utilises multicore CPUs by employing multi-threading, allows for secure SSL/TLS communications.

distributed-systems text machine multithreading load-balancer ssl-support preprocessing tuning-parameters translation-service tls-support postprocessing translation-server
  • Updated Nov 29, 2018
  • C++

Cheejyg / Genetic-Algorithms-for-Swarm-Parameter-Tuning

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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.

genetics fyp tuning genetic-programming swarm genetic-algorithms ntu tuning-parameters final-year-project swarm-intelligence parameter-tuning nanyang-technological-university
  • Updated Dec 2, 2019
  • Python

DavidCico / Boston-House-Prices-With-Regression-Machine-Learning-and-Keras-Deep-Learning

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In this repository, a regression analysis is conducted using different machine learning and deep learning models. The study is led in order to choose the most suitable model by looking at different characteristics (models tuning, features scaling, etc).

machine-learning deep-learning machine-learning-algorithms hyperparameter-optimization standardization regression-models tuning-parameters keras-neural-networks boston-housing-price-prediction linear-models keras-tensorflow regression-analysis ensemble-models
  • Updated Mar 7, 2019
  • Jupyter Notebook

umayrh / sparktuner

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Remove dependency on the package 'humanfriendly'

umayrh
umayrh commented Jan 27, 2019

Is your feature request related to a problem? Please describe.
Generally, it's desirable to keep package dependencies as lean as possible. We use humanfriendly to parse a strings like "50b", "100kb", and "250mb" and convert them to integer values. We should be able to remove this dependency by implementing the parse_size() function in util.py (see [here](https://github.com/umayrh/sparkt

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enhancement good first issue

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