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Showing 50 open source projects for "find"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Opacus
Training PyTorch models with differential privacy
...Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. ... -
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Open Notebook
An Open Source implementation of Notebook LM with more flexibility
Open Notebook is an open-source, privacy-focused alternative to Google’s Notebook LM that gives users full control over their research and AI workflows. Designed to be self-hosted, it ensures complete data sovereignty by keeping your content local or within your own infrastructure. The platform supports 16+ AI providers—including OpenAI, Anthropic, Ollama, Google, and LM Studio—allowing flexible model choice and cost optimization. Open Notebook enables users to organize and analyze... -
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MagicMirror²
Modular smart mirror platform with a list of installable modules
...The core of MagicMirror² contains a strong API which allows 3rd party developers to build additional modules. Modules you can use. Modules you can develop. Read our extensive documentation to find out everything you want to know about the MagicMirror² project. The full API description allows you to build your own modules. On the forum you will find a big list of MagicMirror² enthusiasts. Share your ideas, ask your questions and get support. The perfect place for you to start. MagicMirror² has an extensively documentated API. ... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative)... -
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ml.js
Machine learning tools in JavaScript
...If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often. We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find. -
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TPOT
A Python Automated Machine Learning tool that optimizes ML
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. -
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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. ... -
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KerasTuner
A Hyperparameter Tuning Library for Keras
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms. -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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10
lightning AI
The most intuitive, flexible, way for researchers to build models
...Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any cluster. Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more. -
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Lance
Modern columnar data format for ML and LLMs implemented in Rust
Lance is a columnar data format that is easy and fast to version, query and train on. It’s designed to be used with images, videos, 3D point clouds, audio and of course tabular data. It supports any POSIX file systems, and cloud storage like AWS S3 and Google Cloud Storage. -
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Ray
A unified framework for scalable computing
...Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. ... -
13
Avalanche
End-to-End Library for Continual Learning based on PyTorch
...This includes simple and efficient ways of implementing new continual learning strategies as well as a set of pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison! Avalanche the first experiment of an End-to-end Library for reproducible continual learning research & development where you can find benchmarks, algorithms, etc. -
14
Metarank
A low code Machine Learning service that personalizes articles
...It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools. Metarank makes it easy not only for Amazon to do personalization but for everyone else. Ingest historical item listings, clicks and item metadata so Metarank can find hidden dependencies in the data using our simple JSON format.No Machine Learning experience is required, run our CLI tool with a set of features in a YAML configuration. Run Metarank API service, feed it with real-time events and receive a personalized ranking for your items that will boost conversion, click-through rate or any other business-critical metric you define. -
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Deepchecks
Test Suites for validating ML models & data
...Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. An ordered collection of checks, that can have conditions added to them. ... -
16
Orion
A machine learning library for detecting anomalies in signals
...Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. Abstract away from the users the nitty-gritty about preprocessing, finding the best pipeline, and postprocessing. We want to provide a systematic way to evaluate the latest and greatest machine learning methods via our benchmarking effort. ... -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
...PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy - see the accompanying tutorial. Head over to our documentation to find out more about installation, creation of datasets and a full list of implemented methods and available datasets. -
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satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
...Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful please give it a star and consider sponsoring it. You can also follow me on Twitter and LinkedIn where I aim to post frequent updates on my new discoveries, and I have created a dedicated group on LinkedIn. I have also started a blog here and have published a post on the history of this repository called Dissecting the satellite-image-deep-learning repo. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
...It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. It is no black box, as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model). -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training... -
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FLAML
A fast library for AutoML and tuning
...For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space, and metric), or full customization (arbitrary training and evaluation code). It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. ... -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep... -
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find-similar
User-friendly library to find similar objects
The mission of the FindSimilar project is to provide a powerful and versatile open source library that empowers developers to efficiently find similar objects and perform comparisons across a variety of data types. Whether dealing with texts, images, audio, or more, our project aims to simplify the process of identifying similarities and enhancing decision-making. https://github.com/findsimilar/find-similar - GitHub repo http://demo.findsimilar.org/ - Demo project and tutorial https://docs.findsimilar.org/ - Documentation -
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Paper-with-Code-of-Wireless-comm
Paper-with-Code-of-Wireless-communication-Based-on-DL
Paper-with-Code-of-Wireless-communication-Based-on-DL is a curated repository that collects research papers and corresponding code implementations related to the application of deep learning in wireless communication systems. The project aims to help researchers and graduate students quickly find reproducible implementations of algorithms used in modern communication research. Wireless communication research has increasingly adopted deep learning techniques to address complex tasks such as channel estimation, resource allocation, signal detection, and modulation classification. However, many academic publications do not release source code, which makes it difficult for new researchers to reproduce results or experiment with the proposed methods. ... -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.
