Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
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Jan 5, 2023 - Python
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Isn't that what we all want? Our money to go many? Well that's what this framework/strategy hopes to do for you! By giving you & HyperOpt a lot of signals to alter the weights from.
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Code repository for the online course Hyperparameter Optimization for Machine Learning
AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.
Using Kafka-Python to illustrate a ML production pipeline
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
tensorflow keras cnn hyperparameter-optimization hyperopt hyperparameters-optimization hyperparameter-tuning cnn-keras hyperparameter-search
Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。
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Allstate Kaggle Competition ML Capstone Project
data-science machine-learning udacity tutorial neural-network notebook keras capstone kaggle xgboost keras-tutorials mlp hyperopt cudnn
ES6 hyperparameters search for tfjs
Predict traffic flow with LSTM. For experimental purposes only, unsupported!
🎛 Distributed machine learning made simple.
Time Series Forecasting for the M5 Competition
lightgbm hyperopt prophet demand-forecasting altair time-series-analysis vector-autoregression kats deepar tsfresh gluonts
An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀
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Freqtrade fork for algorithmic trading on Hyperliquid perpetual futures: multi-bot OHLCV/pairlist caching, overfitting-resistant hyperopt (PlateauSampler, walk-forward, CPCV), custom losses, liquidation detection and showcase strategies. By Freqtrade France.
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Machine Learning Tool Box
visualization machine-learning metrics tensorflow keras plot hyperparameter-optimization lightgbm matplotlib hyperopt metric hyperparameter-tuning gradient-boosted-trees
The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.
scikit-learn feature-selection xgboost hyperparameter-optimization lightgbm hyperopt feature-engineering bayesian-optimization hyperparameter-tuning binary-classification imbalanced-data automl loan-default-prediction udacity-machine-learning-nanodegree f1-score automated-feature-engineering featuretools auc-roc-curve auc-roc-score k-fold-cross-validation
Bayesian Optimization for Categorical and Continuous Inputs
machine-learning optimization thompson-sampling hyperparameter-optimization hyperopt gaussian-processes bayesian-optimization multi-armed-bandits hyperparameter-tuning automl automated-machine-learning smac categorical-variables continuous-variable acquisition-functions gpyopt batch-bayesian-optimization
Different hyperparameter optimization methods to get best performance for your Machine Learning Models
Free automated deep learning for spreadsheets
Fair quantitative comparison of NLP embeddings from GloVe to RoBERTa with Sequential Bayesian Optimization fine-tuning using Flair and SentEval. Extension of HyperOpt library to log_b priors.
natural-language-processing word2vec pytorch embeddings transformer seq2seq hyperopt glove bayesian-optimization flair bert roberta elmo transformer-xl senteval
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