Awesome GitHub RepositoriesHyperparameter Optimization Tools

Recommenders is a recommendation system framework designed for building, benchmarking, and deploying collaborative and content-based filtering models. It provides a machine learning model pipeline that standardizes the process of moving recommendation data from raw ingestion through training and evaluation. The project functions as a model benchmarking toolkit, utilizing standardized ranking and error metrics to compare the accuracy of different algorithms. It also serves as a hyperparameter tuning tool, allowing for the optimization of model behavior and performance via external configuratio

This repository serves as a comprehensive educational resource and study guide for mastering deep learning principles and neural network architectures. It provides a structured curriculum that covers the fundamental components of artificial intelligence, including backpropagation, optimization algorithms, and model performance tuning. The collection distinguishes itself by offering curated academic materials and practical implementation examples that bridge the gap between theoretical concepts and hands-on application. It includes specialized instructional guides for developing models capable

ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque

This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur

Surprise is a Python library for building and analyzing recommendation systems. It provides a comprehensive toolkit for implementing collaborative filtering to predict user preferences and generate item suggestions based on historical rating patterns. The library includes dedicated tools for hyperparameter optimization and model evaluation. It allows for searching through parameter sets to find the most effective configurations and utilizes a suite of metrics to measure prediction accuracy. The framework covers the full development workflow, including data loading from various sources, the c

Skorch es una biblioteca que envuelve redes neuronales de PyTorch en una interfaz compatible con scikit-learn, permitiendo que los modelos de aprendizaje profundo se utilicen dentro de pipelines de machine learning estándar y herramientas de optimización de hiperparámetros. Funciona como un adaptador de datos, gestor de entrenamiento y herramienta de optimización que cierra la brecha entre los módulos de aprendizaje profundo y los flujos de trabajo de machine learning convencionales. El proyecto se distingue por proporcionar un kit de herramientas para automatizar el ciclo de vida de entrenamiento de PyTorch, incluyendo checkpointing integrado, parada temprana y programación de tasas de aprendizaje. Además, permite el aprendizaje por transferencia (transfer learning) mediante utilidades para congelar capas específicas del modelo y ajustar pesos preentrenados para tareas especializadas. La superficie de capacidades se extiende a la transformación de datos, incluyendo la conversión de datos tabulares y arrays numéricos a formatos de tensor y el registro de tokenizadores de texto. También proporciona herramientas para la selección de aceleración por hardware, compilación de módulos just-in-time y modelado de datos probabilísticos para la cuantificación de la incertidumbre. El sistema incluye utilidades para mapear hiperparámetros a argumentos de línea de comandos para garantizar experimentos reproducibles.

FLAML is an automated machine learning framework, hyperparameter optimization tool, and large language model agent orchestrator. It provides a system for model selection and tuning across various learners and datasets, while also offering a toolkit for optimizing the inference parameters and fine-tuning settings of large language models. The project features a meta-learning tuning system that analyzes historical task data to generate data-dependent default configurations, accelerating model convergence. It further enables the design of collaborative multi-agent systems through conversational

This project is a collection of scripts and workflows for training, fine-tuning, and deploying large language models using the Hugging Face Transformers toolkit. It functions as a distributed training framework, a library for natural language processing task implementations, and a system for building retrieval-augmented generation chatbots. The repository includes specialized tools for model optimization, such as a Bayesian hyperparameter optimizer for automatically tuning model settings. It provides implementations for scaling model training across multiple graphics processors using data par

This project is a collection of pretrained reinforcement learning agents and training scripts built on Stable Baselines3 and Gymnasium. It provides a framework for training agents to solve specific tasks, managing experiment reproducibility, and deploying pretrained models. The system includes a specialized benchmarking suite and optimization tools for tuning agent settings. It utilizes automated search spaces and distributed trials to maximize performance, while employing bootstrap sampling to generate statistically robust performance metrics and confidence intervals. Broad capabilities cov

Este proyecto es un framework para implementar destilación de conocimiento y gestionar experimentos de deep learning dentro del ecosistema PyTorch. Proporciona un entorno estructurado para entrenar modelos estudiantes compactos que imiten las distribuciones de salida de modelos profesores más grandes, con el objetivo de mejorar la eficiencia de la inferencia. El framework se distingue por desacoplar las arquitecturas de los modelos de las funciones de pérdida, permitiendo una composición flexible de componentes de profesor y estudiante. Integra capacidades de búsqueda de cuadrícula de hiperparámetros automatizada para identificar sistemáticamente configuraciones de entrenamiento óptimas, las cuales son gestionadas mediante serialización de archivos externos para asegurar la consistencia a través de las ejecuciones experimentales. Más allá de sus funciones centrales de destilación y optimización, el toolkit incluye una gestión integral del ciclo de vida para las sesiones de entrenamiento. Esto incluye checkpointing persistente para recuperación ante fallos, monitoreo de progreso en tiempo real y la agregación de métricas de rendimiento en resúmenes estructurados para análisis comparativo.