# Automated Model Selection and Tuning

> Search results for `automated model selection and hyperparameter tuning` on awesome-repositories.com. 116 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/automated-model-selection-and-hyperparameter-tuning

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## Results

- [jakevdp/pythondatasciencehandbook](https://awesome-repositories.com/repository/jakevdp-pythondatasciencehandbook.md) (48,561 ⭐) — This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping.

The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
- [d2l-ai/d2l-en](https://awesome-repositories.com/repository/d2l-ai-d2l-en.md) (29,001 ⭐) — This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation.

The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
- [clearml/clearml](https://awesome-repositories.com/repository/clearml-clearml.md) (6,740 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts.

The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and
- [jamescj60/universal-x86-tuning-utility](https://awesome-repositories.com/repository/jamescj60-universal-x86-tuning-utility.md) (2,452 ⭐) — Universal-x86-Tuning-Utility is a system tuning tool for x86 hardware that adjusts CPU, GPU, and memory settings to optimize performance and power consumption. It provides an adaptive power optimization algorithm that dynamically adjusts processor power limits based on real-time temperature monitoring, balancing performance with thermal safety margins. The utility also includes a hardware specification viewer that displays detailed system information for reference.

The tool distinguishes itself through event-driven profile automation, which applies pre-configured tuning profiles automatically
- [apple/turicreate](https://awesome-repositories.com/repository/apple-turicreate.md) (11,171 ⭐) — This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data.

The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit
- [liyanghart/hyperparameter-optimization-of-machine-learning-algorithms](https://awesome-repositories.com/repository/liyanghart-hyperparameter-optimization-of-machine-learning-algorithms.md) (1,334 ⭐) — Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
- [selectize/selectize.js](https://awesome-repositories.com/repository/selectize-selectize-js.md) (13,022 ⭐) — Selectize.js is a jQuery-based autocomplete library and tagging interface component. It functions as a searchable selection tool that combines a text input field with a dropdown select box to facilitate fast item lookup and the management of discrete tags.

The project specializes in remote data input, allowing it to fetch, rank, and integrate options from a server in real-time as a user types. It utilizes a weighted search ranking system to score and sort results by scanning multiple text fields for relevance.

The library covers a broad range of user interface capabilities, including multi-s
- [gokumohandas/made-with-ml](https://awesome-repositories.com/repository/gokumohandas-made-with-ml.md) (48,343 ⭐) — Made-With-ML is an automated documentation generator and developer experience platform designed to transform source code into structured, searchable reference websites. It functions as a codebase intelligence tool that parses implementation details to provide clear explanations of logic and data requirements.

The system distinguishes itself by leveraging language-level type annotations and structured code comments to generate interface specifications. By utilizing static analysis to extract metadata, it automates the transformation of docstrings into web-ready documentation, ensuring that tec
- [sliang11/active-model-selection-for-putsc](https://awesome-repositories.com/repository/sliang11-active-model-selection-for-putsc.md) (0 ⭐) — This repository holds the source code and raw experimental results of our ICDE 2020 paper "Active Model Selection for Positive Unlabeled Time Series Classification". This repository has the following four folders.
- [automl/auto-sklearn](https://awesome-repositories.com/repository/automl-auto-sklearn.md) (8,111 ⭐) — 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
- [dlr-rm/rl-baselines3-zoo](https://awesome-repositories.com/repository/dlr-rm-rl-baselines3-zoo.md) (2,725 ⭐) — 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
- [uber/ludwig](https://awesome-repositories.com/repository/uber-ludwig.md) (11,718 ⭐) — Ludwig is a declarative machine learning framework designed for training neural networks and large language models using configuration files instead of manual coding. It functions as a multimodal model builder and a low-code tool for supervised fine-tuning, allowing users to build models that process mixed inputs of text, images, audio, and tabular data.

The project distinguishes itself through an automated hyperparameter optimizer and a system for large language model fine-tuning using parameter-efficient adapters. It features a multimodal data pipeline and the ability to automatically gener
- [ng-select/ng-select](https://awesome-repositories.com/repository/ng-select-ng-select.md) (3,378 ⭐) — :star: Native angular select component
- [evandrolg/selecting](https://awesome-repositories.com/repository/evandrolg-selecting.md) (96 ⭐) — :fishing_pole_and_fish: A library that allows you to access the text selected by the user
- [h2oai/h2o-3](https://awesome-repositories.com/repository/h2oai-h2o-3.md) (7,493 ⭐) — 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
- [huggingface/transformers](https://awesome-repositories.com/repository/huggingface-transformers.md) (161,630 ⭐) — Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference.

The library features extensive support for model optimization and
- [ludwig-ai/ludwig](https://awesome-repositories.com/repository/ludwig-ai-ludwig.md) (11,717 ⭐) — Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code.

The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i
- [thudm/p-tuning](https://awesome-repositories.com/repository/thudm-p-tuning.md) (939 ⭐) — 🌟 [2022-10-06] Thrilled to present GLM-130B: An Open Bilingual Pre-trained Model. It is an open-sourced LLM outperforming GPT-3 175B over various benchmarks. Get model weights and do inference and P-Tuning with only 4 RTX 3090 or 8 RTX 2080 Ti FOR FREE!
- [google-gemini/gemini-cli](https://awesome-repositories.com/repository/google-gemini-gemini-cli.md) (105,341 ⭐) — This project provides a command-line interface for managing autonomous agent workflows, task orchestration, and system-level automation. It includes a comprehensive framework for defining agent skills, managing persistent memory, and delegating tasks to specialized subagents. Users can configure complex planning modes, execute shell commands with safety constraints, and integrate external tools through standardized protocols.

The platform supports non-interactive execution via a headless mode and provides an event-driven hook framework for custom lifecycle automation. It features centralized
- [hiyouga/llama-efficient-tuning](https://awesome-repositories.com/repository/hiyouga-llama-efficient-tuning.md) (72,239 ⭐) — This project is a fine-tuning framework and training pipeline designed to optimize and adapt large language and vision models. It provides a specialized toolkit for parameter-efficient tuning and supervised learning, serving as both a trainer for multimodal models and a deployment tool for serving fine-tuned models via high-performance inference engines.

The framework focuses on reducing memory and compute requirements by updating a small subset of model parameters. It supports a wide range of adaptation strategies, including vision-language model training to align text, image, video, and aud
- [nicolashug/surprise](https://awesome-repositories.com/repository/nicolashug-surprise.md) (6,793 ⭐) — 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
- [helicone/helicone](https://awesome-repositories.com/repository/helicone-helicone.md) (5,830 ⭐) — Helicone is an open-source AI gateway and observability platform that provides a unified proxy for routing requests to over 100 LLM providers, combined with comprehensive logging, monitoring, and cost tracking for every call. It functions as a central interception layer that captures request and response data, latency, token usage, and errors across providers, making it possible to observe and debug all LLM interactions from a single dashboard. The platform also includes a prompt management system for versioning, deploying, and A/B testing prompt templates without code changes, and an evaluati
- [timescale/timescaledb-tune](https://awesome-repositories.com/repository/timescale-timescaledb-tune.md) (501 ⭐) — A tool for tuning TimescaleDB for better performance by adjusting settings to match your system's CPU and memory resources.
- [hyperopt/hyperopt](https://awesome-repositories.com/repository/hyperopt-hyperopt.md) (7,582 ⭐) — Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions. It operates as a stochastic search space engine that finds optimal input parameters by searching through real-valued, discrete, and conditional spaces.

The framework distinguishes itself through its support for complex search space configurations, allowing for conditional parameter hierarchies where specific hyperparameters are sampled only if their parent parameters meet certain criteria. It is built as an asynchronous optimization framework, decoupling the generation of searc
- [allegroai/clearml](https://awesome-repositories.com/repository/allegroai-clearml.md) (6,733 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving.

The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
- [thudm/p-tuning-v2](https://awesome-repositories.com/repository/thudm-p-tuning-v2.md) (2,078 ⭐) — An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks
- [open-edge-platform/anomalib](https://awesome-repositories.com/repository/open-edge-platform-anomalib.md) (5,871 ⭐) — Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings.

The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi
- [nyandwi/machine_learning_complete](https://awesome-repositories.com/repository/nyandwi-machine-learning-complete.md) (4,983 ⭐) — This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi
- [kilo-org/kilocode](https://awesome-repositories.com/repository/kilo-org-kilocode.md) (15,616 ⭐) — Kilocode is an autonomous engineering platform designed to orchestrate AI agents for complex software development tasks. It functions as a comprehensive system for automating coding, testing, and repository management by integrating directly with your codebase and terminal. The platform provides a unified gateway for model orchestration, allowing for the management of agentic workflows, event-driven automation, and persistent session state across distributed development environments.

The platform distinguishes itself through its federated task management and policy-based access control, which
- [pgssoft/automate](https://awesome-repositories.com/repository/pgssoft-automate.md) (291 ⭐) — Swift framework containing a set of helpful XCTest extensions for writing UI automation tests
- [hvass-labs/tensorflow-tutorials](https://awesome-repositories.com/repository/hvass-labs-tensorflow-tutorials.md) (9,266 ⭐) — TensorFlow-Tutorials is a collection of educational resources and guided tutorials for implementing machine learning models using the TensorFlow framework. It provides instructional material and videos for building deep learning architectures across diverse domains, including computer vision, natural language processing, and time-series prediction.

The project offers practical guides for developing specific applications such as image captioning, style transfer, and machine translation. It emphasizes a structured approach to learning, ranging from simple linear models to complex reinforcement
- [etcd-io/etcd](https://awesome-repositories.com/repository/etcd-io-etcd.md) (51,838 ⭐) — etcd is a distributed, strongly consistent key-value store designed to provide reliable storage for critical system metadata and coordination primitives. It functions as a distributed consensus engine, utilizing a replicated log and leader-based state machine to ensure that all nodes in a cluster maintain a synchronized view of data. By providing atomic operations and linearizable reads and writes, it serves as a foundational component for distributed systems requiring high availability and fault tolerance.

The system distinguishes itself through its multi-version concurrency control, which e
- [sofie-automation/sofie-tv-automation](https://awesome-repositories.com/repository/sofie-automation-sofie-tv-automation.md) (424 ⭐) — Sofie is a web-based TV automation system for studios and live shows, used in daily live TV news productions by the Norwegian public service broadcaster NRK since September 2018.
- [javascript-tutorial/en.javascript.info](https://awesome-repositories.com/repository/javascript-tutorial-en-javascript-info.md) (25,344 ⭐) — This project is a comprehensive JavaScript programming tutorial and language reference. It serves as a web development education resource providing instruction on modern language fundamentals, object-oriented design, and advanced asynchronous programming patterns.

The resource functions as both a frontend development guide and a technical reference. It covers core language features such as closures, prototypes, promises, and typed arrays, while providing practical lessons on managing browser data and handling network requests.

The content spans several key capability areas, including browser
- [erykpiast/autocompleted-select](https://awesome-repositories.com/repository/erykpiast-autocompleted-select.md) (12 ⭐) — autocompleted-select
- [optuna/optuna](https://awesome-repositories.com/repository/optuna-optuna.md) (14,388 ⭐) — 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
- [livekit/livekit](https://awesome-repositories.com/repository/livekit-livekit.md) (19,358 ⭐) — LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with users through voice, video, and text. It provides a centralized, event-driven architecture to manage the entire lifecycle of automated participants, from initialization and session state management to graceful shutdown. By utilizing a selective forwarding unit, the platform efficiently routes media streams between participants and agents, ensuring low-latency communication and secure, token-based authentication for all connections.

The platform distinguishes itself through it
- [v-rusu/tuning-fork](https://awesome-repositories.com/repository/v-rusu-tuning-fork.md) (3 ⭐) — A configurable client-side JavaScript library for guitar tuning with real-time pitch detection. Supports standard and alternate tunings for guitar, bass, ukulele, banjo, and custom instruments.
- [keras-team/keras](https://awesome-repositories.com/repository/keras-team-keras.md) (64,094 ⭐) — Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management.

The project distinguishes itself as a multi-backend machine learning
- [besya/flipperzero-tuning-fork](https://awesome-repositories.com/repository/besya-flipperzero-tuning-fork.md) (91 ⭐) — Tuning Fork for Flipper Zero
- [filamentphp/filament](https://awesome-repositories.com/repository/filamentphp-filament.md) (31,215 ⭐) — Filament is a full-stack framework for building administrative panels and management interfaces within the Laravel ecosystem. It provides a declarative, component-based architecture that allows developers to construct complex, data-driven applications using server-side configuration objects rather than manual HTML. By inspecting database model structures and relationships, the framework automates the generation of CRUD interfaces, forms, and data tables, significantly reducing boilerplate code.

The project distinguishes itself through a highly modular and extensible design that supports custo
- [apachecn/interview](https://awesome-repositories.com/repository/apachecn-interview.md) (8,944 ⭐) — This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings.

The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologie
- [crewaiinc/crewai](https://awesome-repositories.com/repository/crewaiinc-crewai.md) (53,687 ⭐) — CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations.

The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo
- [snapappointments/bootstrap-select](https://awesome-repositories.com/repository/snapappointments-bootstrap-select.md) (9,826 ⭐) — Bootstrap Select is a jQuery plugin that replaces standard HTML select elements with a stylized interface. It functions as a custom dropdown menu that transforms native browser inputs into accessible form components.

The project differentiates itself by providing real-time search filtering, multi-select data entry, and the ability to populate selection lists dynamically via remote JSON sources. To maintain browser performance when handling large datasets, it utilizes virtual rendering to display only the visible subset of options.

The component includes accessibility support through ARIA att
- [ageron/handson-ml](https://awesome-repositories.com/repository/ageron-handson-ml.md) (25,608 ⭐) — This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning.

The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o
- [holms-ur/fine-tuning](https://awesome-repositories.com/repository/holms-ur-fine-tuning.md) (72 ⭐) — Close-Domain fine-tuning for table detection
- [deepinsight/insightface](https://awesome-repositories.com/repository/deepinsight-insightface.md) (29,002 ⭐) — InsightFace is a comprehensive deep learning framework designed for face recognition, biometric identity verification, and feature extraction. It provides a specialized engine for one-to-one verification and one-to-many identification tasks, utilizing convolutional neural networks to transform raw image pixels into high-dimensional vector embeddings. The project includes a complete toolkit for detecting, aligning, and processing facial data to ensure consistent identity discrimination.

Beyond core recognition, the platform distinguishes itself through an extensive model management and optimiz
- [cgzirim/seek-tune](https://awesome-repositories.com/repository/cgzirim-seek-tune.md) (5,583 ⭐) — Seek-Tune is an audio fingerprinting library that implements a Shazam-like algorithm for identifying songs from audio recordings. It generates acoustic fingerprints from audio signals and matches them against a known database to recognize songs.

The library converts audio into a time-frequency spectrogram using FFT-based frequency analysis, then extracts peak points to create compact, unique fingerprints for each song. It uses combinatorial hashing to combine nearby peak pairs into hash values with time offsets, enabling efficient database lookup and matching through a peak-pair matching algo
- [amruthpillai/reactive-resume](https://awesome-repositories.com/repository/amruthpillai-reactive-resume.md) (38,613 ⭐) — This project is a web-based platform designed for creating, managing, and sharing professional resumes. It functions as a structured document builder that integrates artificial intelligence to assist with content generation, editing, and analysis. Users can maintain a collection of resumes, customize their visual presentation through various templates, and export them into multiple formats for job applications.

The platform distinguishes itself through its autonomous AI agent capabilities, which can perform research, suggest incremental edits, and apply data patches directly to documents. It
- [awslabs/autogluon](https://awesome-repositories.com/repository/awslabs-autogluon.md) (10,481 ⭐) — AutoGluon is an automated machine learning framework designed to optimize model selection and hyperparameter tuning across tabular, text, image, and time series data. It functions as an ensemble learning library and a tabular data prediction engine, aiming to build high-accuracy predictive models without manual algorithm selection.

The framework integrates multimodal machine learning pipelines that combine disparate data types into a single representation using specialized encoders. It also includes a probabilistic time series forecaster that fits multiple statistical and deep learning models