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Published directory entries matching your search.
A music discovery site that lists the most popular music on the web right now, based on posts from thousands of people who write about music they love. The popular page shows tracks with the number of sites that posted them and links to Bandcamp and other platforms. It includes sections such as Latest, Popular, Indexed Sites, Spy, and Archives.
This repository provides a hyperparameter optimization implementation for machine learning algorithms, accompanying a paper on hyperparameter optimization theory and practice. It includes sample code for regression and classification problems using the Boston-Housing and MNIST datasets, covering algorithms such as random forest, SVM, KNN and artificial neural networks.
Hyperlane is an open framework for blockchain interoperability. It supports bridging, swapping and sending arbitrary data across more than 150 chains and 7 virtual machines, including EVM, Solana, Cosmos and custom VM architectures. It also covers interchain messaging, multichain asset issuance and cross-chain governance.
Hypernets is a general AutoML framework for building automatic optimization tools for machine learning frameworks and libraries, including tensorflow, keras, pytorch, sklearn, lightgbm and xgboost. It offers an abstract search space representation covering hyperparameter optimization and neural architecture search, with optimization algorithms such as evolution algorithm and Monte Carlo tree search.
Hyperopt is an open-source Python library available on GitHub, designed to provide distributed asynchronous hyperparameter optimization functionality for tuning machine learning models in development workflows, production environments, as well as research settings.
A TechPolicy.Press discussion with Arvind Narayanan and Sayash Kapoor, authors of AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference, published September 24 by Princeton University Press. The conversation covers AI hype and how to distinguish AI that does not work from real promise.
Hyperas is a wrapper that combines Keras model definition with Hyperopt hyperparameter optimization. Users define models as usual and wrap parameters in double curly brackets with a chosen distribution. It is installed via pip and runs optimization using algorithms such as TPE with a set number of evaluation runs.
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. It works with PyTorch and TensorFlow, offering a Python library, command line interface, and web UI for managing machine learning workflows.
NNI is an open source AutoML toolkit developed by Microsoft. It automates tasks across the machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning for machine learning workflows.
Early days of AI is a blog post by Elad Gil that discusses the early development of machine learning systems and the AI hype cycle. Gil argues that recent advances like transformers and diffusion models represent a new era, distinct from previous AI. He draws on his experience at Google, Twitter, and as an investor.
FLAML is a lightweight Python library from Microsoft for automating machine learning and AI operations. It performs model selection and hyperparameter optimization under resource constraints, including tuning foundation model inference hyperparameters. It requires Python 3.10 or later, is installable via pip, and can be used as a scikit-learn style estimator.
FlexML is an AutoML library for Python that assists in building machine learning models by automating model selection and hyperparameter tuning. It supports regression and classification tasks and offers quick and wide experiment modes for choosing how many models to train.
Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML). It supports hyperparameter tuning, early stopping and neural architecture search, and is agnostic to machine learning frameworks, natively supporting frameworks such as TensorFlow, PyTorch and XGBoost. Training jobs can use any Kubernetes Custom Resources, with out-of-box support for Kubeflow Training Operator, Argo Workflows and Tekton Pipelines. A Python SDK simplifies creating tuning jobs.
mljar-supervised is an Automated Machine Learning Python package for tabular data. It automates data preprocessing, model construction and hyperparameter tuning, and produces a Markdown report for each model. It offers Explain, Perform and Compete modes, along with model explanations and generated web apps for predictions.
Optuna is an open source automatic hyperparameter optimization software framework designed for machine learning. It provides an imperative, define-by-run style user API with eager search spaces, algorithms that prune unpromising trials, and parallelization of searches over multiple threads or processes without modifying code. It is framework agnostic and can be installed with pip on Python 3.10 or newer.
Tune is a Python library for experiment execution and hyperparameter tuning at any scale. It supports machine learning frameworks including PyTorch, XGBoost, and TensorFlow and Keras, and runs algorithms such as Population Based Training (PBT) and HyperBand/ASHA. It also integrates with hyperparameter optimization tools including Ax, BayesOpt, BOHB, Nevergrad, and Optuna.
Advisor is an open-source GitHub repository maintained by user tobegit3hub. It provides an open-source implementation of Google Vizier built for hyperparameter tuning tasks. The repository includes documentation for supported algorithms and a quick start guide for users.
autoai is an open-source Python framework for automatic artificial intelligence focused on regression and classification tasks over numerical data. It automates model search, hyper-parameter tuning, and generates high-quality Jupyter Notebook code for resulting models.
automl-gs is a Python 3 AutoML module that takes an input CSV and a target field and generates a trained machine learning or deep learning model plus native Python code pipelines. It provides a zero code/model definition interface and performs random hyperparameter searches, reporting metrics for each trial in a CSV.
Awesome AutoDL is a curated list of automated deep learning resources and an in-depth analysis, subtitled that neural architecture search is not the end. It collects papers, libraries and benchmarks covering neural architecture search and hyper-parameter optimization.
Goptuna is a hyperparameter optimization framework inspired by Optuna, written in pure Go. It provides Bayesian optimization, evolution strategies and multi-armed bandit algorithms, including random search, TPE, CMA-ES variants, ASHA and Sobol sampling. It supports a Define-by-Run style API for dynamically constructing search spaces and offers RDB storage for continuing optimizations and scaling studies across workers.
Framework for distributed hyperparameter optimization.
Library for hyperparameter optimization and black box optimization benchmarks.
hyperopt (hyperopt-sklearn) is an open-source project that provides hyper-parameter optimization functionality for the scikit-learn (sklearn) machine learning library. It is hosted as a public repository on GitHub for collaborative development.
