Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 12
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Showing 1130 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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ToogleBox: Simplify, Automate and Improve Google Workspace FunctionalitiesToogleBox was created as a solution to address the challenges faced by Google Workspace Super Admins. We developed a premium and secure Software-as-a-Service (SaaS) product completely based on specific customer needs. ToogleBox automates most of the manual processes when working with Google Workspace functionalities and includes additional features to improve the administrator experience.
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MaintainX is the world-leading mobile-first workflow management platform for industrial and frontline workers.Your day-to-day maintenance tasks, simplified. MaintainX eliminates the paperwork, so you can spend less time on your clipboard and more time getting things done.
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doccano
Open source annotation tool for machine learning practitioners
doccano is an open-source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence-to-sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours. -
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AutoViz
Automatically Visualize any dataset, any size
AutoViz is a Python data visualization library designed to automate exploratory data analysis by generating multiple visualizations with minimal code. The primary goal of the project is to help data scientists and analysts quickly understand patterns, relationships, and anomalies within datasets without manually writing complex plotting code. With a single command, the library can automatically generate dozens of charts and graphs that reveal insights into the structure and quality of the data. -
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refinery
Open-source choice to scale, assess and maintain natural language data
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact. You are one of the people we've built refinery for. refinery helps you to build better NLP models in a data-centric approach. Semi-automate your labeling, find low-quality subsets in your training data, and monitor your data in one place. refinery doesn't get rid of manual labeling, but it makes sure that your valuable time is spent well. ... -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates those automatically based on a simple configuration. It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. ... -
Contractor Foreman is the most affordable all-in-one construction management software for contractors and is trusted by contractors in more than 75 countries.Starting at $49/m for the WHOLE company, Contractor Foreman is the most affordable all-in-one construction management system for contractors. Our customers in 75+ countries and industry awards back it up. And it's all backed by a 100 day guarantee.
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DocETL
A system for agentic LLM-powered data processing and ETL
DocETL is an open-source system designed to build and execute data processing pipelines powered by large language models, particularly for analyzing complex collections of documents and unstructured datasets. The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. -
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PEFT
State-of-the-art Parameter-Efficient Fine-Tuning
...Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning. -
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EconML
Python Package for ML-Based Heterogeneous Treatment Effects Estimation
EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. One of the biggest promises of machine learning is to automate decision-making in a multitude of domains. -
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ROSA
I Agent designed to interact with ROS1- and ROS2-based robotics system
...This capability enables users to inspect system status, diagnose issues, and control robot behavior without manually navigating complex command-line tools or configuration files. The system integrates with robotics software stacks and exposes operational tools that allow AI agents to analyze system logs, inspect sensors, or trigger robot tasks. -
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MySQL MCP Server
A Model Context Protocol (MCP) server that enables secure interaction
The MySQL MCP Server enables secure interaction with MySQL databases, allowing AI assistants to list tables, read data, and execute SQL queries through a controlled interface. It is designed for integration with AI applications like Claude Desktop and should not be run as a standalone Python program. -
Intelligent Retail ManagementQuant offers a wide range of solutions for retail. Within one integrated software system, it allows you to efficiently combine the management of retail space, shelf labels and marketing materials with task management, reporting and automatic replenishment.
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MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements. In MLRun the assets, metadata, and services (data, functions, jobs, artifacts, models, secrets, etc.) are organized into projects. ... -
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DeepLabCut
Implementation of DeepLabCut
DeepLabCut™ is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates or for multi-animals. Please see the original paper and the latest work below! ... -
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LLMStack
No-code multi-agent framework to build LLM Agents, workflows
LLMStack is a no-code platform for building generative AI agents, workflows and chatbots, connecting them to your data and business processes. Build tailor-made generative AI agents, applications and chatbots that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data, internal tools and GPT-powered models without any coding experience using LLMStack's no-code builder. Trigger your AI chains from Slack or Discord. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
...Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. Access to all intermediate feature maps is beneficial if you want to apply deep supervision losses on them or encoder-decoder of object detection task. -
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OpenDataMCP
Connect any Open Data to any LLM with Model Context Protocol
An initiative aimed at connecting open datasets to Large Language Models (LLMs) using the Model Context Protocol, facilitating seamless access and integration of public data into AI applications. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
...All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. 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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Matrix
Multi-Agent daTa geneRation Infra and eXperimentation framework
Matrix is a distributed, large-scale engine for multi-agent synthetic data generation and experiments: it provides the infrastructure to run thousands of “agentic” workflows concurrently (e.g. multiple LLMs interacting, reasoning, generating content, data-processing pipelines) by leveraging distributed computing (like Ray + cluster management). The idea is to treat data generation as a “data-to-data” transformation: each input item defines a task, and the runtime orchestrates asynchronous, peer-to-peer agent workflows, avoiding global synchronization bottlenecks. ... -
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Qwen-2.5-VL
Qwen2.5-VL is the multimodal large language model series
...Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation (exceeding 8,000 tokens), and structured data comprehension, such as tables and JSON formats. They support context lengths up to 128,000 tokens and offer multilingual capabilities in over 29 languages, including Chinese, English, French, Spanish, and more. The models are open-source under the Apache 2.0 license, with resources and documentation available on platforms like Hugging Face and ModelScope. -
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Magika
Fast and accurate AI powered file content types detection
Magika is an AI-powered file-type detector that uses a compact deep-learning model to classify binary and textual files with high accuracy and very low latency. The model is engineered to be only a few megabytes and to run quickly even on CPU-only systems, making it practical for desktop apps, servers, and security pipelines. Magika ships as a command-line tool and a library, providing drop-in detection that improves on traditional “magic number” and heuristic approaches, especially for ambiguous or short files. -
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FinGPT
Open-Source Financial Large Language Models
FinGPT is an open-source, finance-specialized large language model framework that blends the capabilities of general LLMs with real-time financial data feeds, domain-specific knowledge bases, and task-oriented agents to support market analysis, research automation, and decision support. It extends traditional GPT-style models by connecting them to live or historical financial datasets, news APIs, and economic indicators so that outputs are grounded in relevant and recent market conditions rather than generic knowledge alone. ... -
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GluonTS
Probabilistic time series modeling in Python
...The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts, by removing the last three years (36 months) from the train data. Thus, we will train a model on just the first nine years of data. Python has the notion of extras – dependencies that can be optionally installed to unlock certain features of a package. We make extensive use of optional dependencies in GluonTS to keep the amount of required dependencies minimal. To still allow users to opt-in to certain features, we expose many extra dependencies. -
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Recommenders
Best practices on recommendation systems
...Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. ... -
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python-small-examples
Focus on creating classic Python small examples and cases
python-small-examples is an open-source educational repository that contains hundreds of concise Python programming examples designed to illustrate practical coding techniques. The project focuses on teaching programming concepts through small, focused scripts that demonstrate common tasks in data processing, visualization, and general programming. Each example highlights a specific function or programming pattern so that learners can quickly understand how to apply Python features in real-world scenarios. The repository includes examples covering topics such as file processing, JSON manipulation, data visualization, and library usage. ... -
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Cube Studio
Cube Studio open source cloud native one-stop machine learning
Cube Studio is an open-source, cloud-native end-to-end machine learning and AI platform designed to support the full lifecycle of AI development — from data preparation and interactive notebook coding to distributed training, model tuning, and deployment in production-ready environments. It provides a unified interface where teams can manage data sources, track datasets, and build pipelines using drag-and-drop workflow orchestration, making it accessible for both engineers and data scientists working at scale. ... -
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DeepSeek VL2
Mixture-of-Experts Vision-Language Models for Advanced Multimodal
...The model supports both image understanding (vision tasks) and multimodal reasoning, and is likely used as a component in agent systems to process visual inputs as context for downstream tasks. The repository includes evaluation results (e.g. image/text alignment scores, common VL benchmarks), configuration files, and model weights (where permitted). While the internal architecture details are not fully documented publicly, the repo suggests that VL2 introduces enhancements over prior vision-language models (e.g. better scaling, cross-modal attention, more robust alignment) to improve grounding and multimodal understanding. -
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PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance.
