A hyperparameter optimization framework, inspired by Optuna.
c-bata/goptuna की मुख्य विशेषताएं हैं: Automated Machine Learning, Machine Learning।
c-bata/goptuna के ओपन-सोर्स विकल्पों में शामिल हैं: rhiever/tpot — This is a Python automated machine learning framework designed to automate the design and optimization of machine… h2oai/h2o-3 — h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and… optuna/optuna — Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine… pycaret/pycaret — PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It… google-research/google-research — This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum… karpathy/autoresearch — Autoresearch is an autonomous machine learning research agent and architecture search framework. It employs a…
Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl
h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i
This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp