Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

Isn't that what we all want? Our money to go many? Well that's what this strategy hopes to do for you! By giving you/HyperOpt a lot of signals to alter the weight from.

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.

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

Using Kafka-Python to illustrate a ML production pipeline

Allstate Kaggle Competition ML Capstone Project

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

ES6 hyperparameters search for tfjs

Predict traffic flow with LSTM. For experimental purposes only, unsupported!

🎛 Distributed machine learning made simple.

Machine Learning Tool Box

Free automated deep learning for spreadsheets

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀

Machine learning's parameter search and feature selection module which is integrated log management and visualization.

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.

The accompanying repo for the hyperparameters optimization bdx meetup talk, blog post and webinar

Reticulate wrapper for hyperopt

NYC taxi trip duration Kaggle submission using fully connected neural network

Bayesian Optimization for Categorical and Continuous Inputs

A hyperopt wrapper - simplifying hyperparameter tuning with Scikit-learn style estimators.

Hyperparameter tuning for FCN using Ray Tune

Repo that relates to the Medium blog 'Using Bayesian Optimization to reduce the time spent on hyperparameter tuning'

Hyper parameter optimization extension for ASReview. EXPERIMENTAL

sklearn wrapper for Hyperopt

Different hyperparameter optimization methods to get best performance for your Machine Learning Models

CIFAR-10 image classification of imbalanced data using bottleneck features extracted from the autoencoder.

Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. Hyperparameters are crucial as they control the overall behavior of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that minimizes a predefined loss function to give better results.

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.

Markdown guides and sample code for various hyperparameter search packages.

predict whether a mortgage application was accepted (meaning the loan was originated) or denied according to the given dataset.