Search Results for "r-studio"
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Showing 27 open source projects for "r-studio"
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1
Label Studio
Label Studio is a multi-type data labeling and annotation tool
...Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. ... -
2
MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. 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. -
3
Elyra
Elyra extends JupyterLab with an AI centric approach
Elyra is a set of AI-centric extensions to JupyterLab Notebooks. The Elyra Getting Started Guide includes more details on these features. A version-specific summary of new features is located on the releases page. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). ... -
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5
Open Notebook
An Open Source implementation of Notebook LM with more flexibility
...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 multi-modal content such as PDFs, videos, audio files, web pages, and Office documents. It combines full-text and vector search with context-aware AI chat to deliver insights grounded in your own research materials. With advanced features like multi-speaker podcast generation, customizable content transformations, and a comprehensive REST API, Open Notebook provides a powerful and extensible research environment. -
6
Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the... -
7
PyCaret
An open-source, low-code machine learning library in Python
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and... -
8
StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery... -
9
AIF360
A comprehensive set of fairness metrics for datasets
...The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R. The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. The tutorials and other notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given use case. ... -
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10
Pedalboard
A Python library for audio
...It supports the most popular audio file formats and a number of common audio effects out of the box and also allows the use of VST3® and Audio Unit formats for loading third-party software instruments and effects. pedalboard was built by Spotify’s Audio Intelligence Lab to enable using studio-quality audio effects from within Python and TensorFlow. Internally at Spotify, pedalboard is used for data augmentation to improve machine learning models and to help power features like Spotify’s AI DJ and AI Voice Translation. pedalboard also helps in the process of content creation, making it possible to add effects to audio without using a Digital Audio Workstation. -
11
GluonTS
Probabilistic time series modeling in Python
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. 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,... -
12
Robyn
Experimental, AI/ML-powered and open sourced Marketing Mix Modeling
Robyn is an open-source, AI/ML-powered Marketing Mix Modeling (MMM) toolkit developed by Meta Marketing Science under the “facebookexperimental” GitHub umbrella. Its goal is to democratize rigorous MMM: what traditionally required expert statisticians and expensive consulting becomes accessible to any company with data. Robyn takes in historical data (spends on different marketing channels, conversions, or revenue, and optional context or organic-media variables) and uses a combination of... -
13
Ubix Linux
The Pocket Datalab
...You can find additional information, technical guidance, and user credentials on the project website https://ubix-linux.sourceforge.io/ or on the project subreddit https://reddit.com/r/UbixLinux. -
14
FEDML Open Source
The unified and scalable ML library for large-scale training
...Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds. A typical workflow is shown in the figure above. When a developer wants to run a pre-built job in Studio or Job Store, TensorOperaLaunch swiftly pairs AI jobs with the most economical GPU resources, and auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. -
15
OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. -
16
Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
17
Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
18
ML workspace
All-in-one web-based IDE specialized for machine learning
...This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. Easy to deploy on Mac, Linux, and Windows via Docker. Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows. -
19
PaddlePaddle models
Pre-trained and Reproduced Deep Learning Models
...An end-to-end development kit that meets the needs of enterprises for low-cost development and rapid integration. The model library of Flying Paddle is an industrial-level model library tailored around the actual R&D process of domestic enterprises, serving enterprises in many fields such as energy, finance, industry, and agriculture. -
20
TensorFlow 2.0 Tutorials
TensorFlow 2.x version's Tutorials and Examples
TensorFlow 2.0 Tutorials is an open-source educational repository that provides practical examples and walkthroughs for learning deep learning using the TensorFlow 2.x framework. The repository contains a large set of hands-on tutorials that demonstrate how to build neural networks and machine learning systems with modern TensorFlow APIs. These examples cover a wide range of topics including convolutional neural networks, recurrent neural networks, generative adversarial networks,... -
21
Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. -
22
TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the... -
23
ChainerCV
ChainerCV: a Library for Deep Learning in Computer Vision
...In ChainerCV, we define the object detection task as a problem of, given an image, bounding box-based localization and categorization of objects. Bounding boxes in an image are represented as a two-dimensional array of shape (R,4), where R is the number of bounding boxes and the second axis corresponds to the coordinates of bounding boxes. ChainerCV supports dataset loaders, which can be used to easily index examples with list-like interfaces. Dataset classes whose names end with BboxDataset contain annotations of where objects locate in an image and which categories they are assigned to. ... -
24
LUMINOTH
Deep Learning toolkit for Computer Vision
...It was created to simplify the process of building and experimenting with deep learning models capable of identifying objects within images. Luminoth includes support for popular object detection architectures such as Faster R-CNN and SSD, enabling developers to train models on datasets like COCO and Pascal VOC. The toolkit provides command-line utilities for dataset management, training, and inference, making it easier to integrate into research workflows and production systems. Although the project is no longer actively maintained, it remains a useful educational and experimental platform for studying object detection pipelines and deep learning workflows. -
25
DSTK - DataScience ToolKit
DSTK - DataScience ToolKit for All of Us
...Of course you may specify JASP for advanced data editing and RapidMiner for advanced prediction modeling. DSTK is written in C#, Java and Python to interface with R, NLTK, and Weka. It can be expanded with plugins using R Scripts. We have also created plugins for more statistical functions, and Big Data Analytics with Microsoft Azure HDInsights (Spark Server) with Livy. License: R, RStudio, NLTK, SciPy, SKLearn, MatPlotLib, Weka, ... each has their own licenses.
