hyperparameter search for machine learning
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Updated
Oct 31, 2017 - Python
hyperparameter search for machine learning
Easy & Quick : Docker services are using hyperopt in parallel on top of Mongodb
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
A wrapper for searching over Neural network architectures, built on top of MXNet framework, in particular gluon.
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Hyperparameter search algorithm
Hyperparameter Optimization Using Sci-kit Optimization
The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset
🔬 Some personal research code on analyzing CNNs. Started with a thorough exploration of Stanford's Tiny-Imagenet-200 dataset.
Code examples for https://blog.floydhub.com/guide-to-hyperparameters-search-for-deep-learning-models/
Code written in Python 2.7 for experiment on SVM with RBF kernel on "Robust and Efficient Kernel Hyperparameter Paths with Guarantees".
ray project 中文文档
Sentiment analysis for amazon product reviews using NLTK, Scikit-Learn, and Keras. Using hyperparameter search and LSTM, our best model achieves ~96% accuracy.
Hyperparameter search for AllenNLP - powered by Ray TUNE
sklearn wrapper for Hyperopt
MLCloudSearch presents tooling built to facilitate the use of Scikit-Learn and Tensorflow Keras Models. The name MLCloudSearch was inspired by the Machine Learning technique of searching for the best parameters to use in fitting a model, commonly referred to as Hyper-Parameter Search. Training can be done locally or on the Cloud.
Efficient AutoRL script for any framework
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Machine Learning Term project of IOG5016 and IOG5018
CS5228 Kaggle Inclass Competition: Predicting if Income > 50K
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