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 framework/strategy hopes to do for you! By giving you & HyperOpt a lot of signals to alter the weights 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.

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

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

Code repository for the online course Hyperparameter Optimization for Machine Learning

Allstate Kaggle Competition ML Capstone Project

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

Machine Learning Tool Box

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

Free automated deep learning for spreadsheets

Bayesian Optimization for Categorical and Continuous Inputs

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

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 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.

A convenient FreqTrade wrapper-library that makes it easy to develop algorithmic trading strategies.