Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
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Updated
Sep 22, 2022 - Jupyter Notebook
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
A Julia machine learning framework
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Time Series Cross-Validation -- an extension for scikit-learn
Parallel Hyperparameter Tuning in Python
🔥 A curated list of awesome links related to MySQL / MariaDB / Percona performance tuning
Open source cross-platform compiler for compute-intensive loops used in AI algorithms, from Microsoft Research
R package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with gaussian process
A friendly python package for Keras Hyperparameters Tuning based only on NumPy and hyperopt.
Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
Easy Hyper Parameter Optimization with mlr and mlrMBO.
A novel Sparse-Coding Based Approach Feature Selection with emphasizing joint l_1,2-norm minimization and the Class-Specific Feature Selection.
A Magisk module for maximizing the digital audio fidelity by reducing jitters on audio outputs (USB DACs, Bluetooth a2dp, DLNA, etc.)
MATLAB code for tuning a PID controller using Genetic Algorithm (GA)
Package for machine learning of astronomical objects such as light curves
Implementation of a genetic algorithm to determine the parameters of the PID, PI-D, I-PD and PIDA controllers in order to compensate various benchmark processes, which are representative of many industrial applications. In particular, by considering separately a set-point and a load disturbance rejection unit step response the IAE is minimized b…
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.
Tuning Monte Carlo generators with machine learning.
Fractional order proportional derivative controller tuner
⚛️ Deep Learning Specialization by deeplearning.ai
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