Image Classification library built on top of Keras. Identifies the best set of hyperparameters and train a classification model accordingly, hence, smart.
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Updated
Aug 2, 2024 - Python
Image Classification library built on top of Keras. Identifies the best set of hyperparameters and train a classification model accordingly, hence, smart.
Markdown guides and sample code for various hyperparameter search packages.
Easy & Quick : Docker services are using hyperopt in parallel on top of Mongodb
Tuning LDA
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Semi-automated machine learning pipelines
A program to optimize the hyperparameters of a machine learning model
Tuning hyperparams fast with Hyperband
ICSE'18: Tuning Smote
HYPO_RFS is an algorithm for performing exhaustive grid-search approach for tuning the hyper-parameters of Ranking Feature Selection (RFS) approaches.
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
IST journal 2017: Tuning LDA
Comparison of FFT, DE_RF against SMOTUNED
LDA, TF-IDF against SVM, RF, DT, FFT (Text Mining)
Bayesian Optimization and Grid Search for xgboost/lightgbm
End-to-End Machine Learning Project
k-Nearest Neighbors Classifier (k-NN)
Wrapper for neural evolution approach for hyperparameter tuning
Moody's Bond Rating Classifier and USPHCI Economic Activity Forecast Modeling
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