Search Results for "computer based training"
Sort By:
Showing 56 open source projects for "computer based training"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
Diffgram
Training data (data labeling, annotation, workflow) for all data types
...Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
2
Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
Datumaro is a flexible Python-based dataset management framework and command-line tool for building, analyzing, transforming, and converting computer vision datasets in many popular formats. It supports importing and exporting annotations and images across a wide variety of standards like COCO, PASCAL VOC, YOLO, ImageNet, Cityscapes, and many more, enabling easy integration with different training pipelines and tools. -
3
EvoTrees.jl
Boosted trees in Julia
A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram-based algorithms with support for multiple loss functions, including various regressions, multi-classification and Gaussian max likelihood. -
4
TensorBoardX
tensorboard for pytorch (and chainer, mxnet, numpy, etc.)
The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training. TensorboardX now supports logging directly to Comet. Comet is a free cloud based solution that allows you to automatically track, compare and explain your experiments. It adds a lot of functionality on top of tensorboard such as dataset management, diffing experiments, seeing the code that generated the results and more. ... -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
-
5
Zingg
Scalable master data management and identity resolution
Zingg is an open-source entity resolution and master data management platform for finding duplicate, related, or matching records across large datasets. It uses machine learning to learn how records should be compared, reducing the need for brittle hand-written matching rules. The project is designed for data engineering and analytics teams working on customer 360, supplier 360, deduplication, fuzzy matching, data quality, and golden record workflows. Zingg runs on Apache Spark and can scale... -
6
Gorse Recommender System Engine
An open source recommender system service written in Go
An open-source recommender system service written in Go. Recommend items from Popular, latest, user-based, item-based and collaborative filtering. Search the best recommendation model automatically in the background. Support horizontal scaling in the recommendation stage after single node training. Support Redis, MySQL, Postgres, MongoDB, and ClickHouse as its storage backend. Expose RESTful APIs for data CRUD and recommendation requests. -
7
XState
State machines and statecharts for the modern web
JavaScript and TypeScript finite state machines and statecharts for the modern web. Statecharts are a formalism for modeling stateful, reactive systems. This is useful for declaratively describing the behavior of your application, from the individual components to the overall application logic. XState is a library for creating, interpreting, and executing finite state machines and statecharts, as well as managing invocations of those machines as actors. The following fundamental computer... -
8
Covalent workflow
Pythonic tool for running machine-learning/high performance workflows
Covalent is a Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, HPC clusters, GPU arrays, and cloud services. Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. Covalent overcomes computational and operational challenges inherent... -
9
Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source... -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
10
Jmol
An interactive viewer for three-dimensional chemical structures.
Over 1,000,000 page views per month. Jmol/JSmol is a molecular viewer for 3D chemical structures that runs in four independent modes: an HTML5-only web application utilizing jQuery, a Java applet, a stand-alone Java program (Jmol.jar), and a "headless" server-side component (JmolData.jar). Jmol can read many file types, including PDB, CIF, SDF, MOL, PyMOL PSE files, and Spartan files, as well as output from Gaussian, GAMESS, MOPAC, VASP, CRYSTAL, CASTEP, QuantumEspresso, VMD, and many other... -
11
PLplot
Cross-platform, scientific graphics plotting library
PLplot is a cross-platform, scientific graphics plotting library that supports math symbols and human languages (via UTF-8 user input strings); plot capabilities for multiple non-interactive plot file formats and in multiple interactive environments; and bindings for multiple computer languages. -
12
Fondant
Production-ready data processing made easy and shareable
Fondant is a modular, pipeline-based framework designed to simplify the preparation of large-scale datasets for training machine learning models, especially foundation models. It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components. ... -
13
json-scada
A portable SCADA/IoT platform centered on the MongoDB database server.
Standard IT tools applied to SCADA/IoT (MongoDB, PostgreSQL/TimescaleDB,Node.js, C#, Golang, Grafana, etc.). MongoDB as the real-time core database, persistence layer, config store, SOE historian. Portability and interoperability over Linux, Windows, x86/64, ARM. Horizontal scalability, from a single computer to big clusters (MongoDB-sharding), Bare Metal, Docker containers, VM, cloud, or hybrid deployments. Unlimited tags, servers, and users. HTML5 Web interface. UTF-8/I18N. Protocols:... -
14
Drawj2d
Draw vector graphics to scale using a descriptive language
Drawj2d creates technical line drawings using a descriptive language. It writes pdf, svg, eps, emf and dxf vector graphics or png images. It runs on all platforms that run Java. It is inspired by Asymptote but with a tcl-like syntax and 2D only. -
15
Analyze and visualize tours in many variations, which are recorded with a GPS device, bike-, exercise- or ergometer-computer. Some highlights: Tools are available to adjust recorded data, GPS tours can be visualized in customizable maps, ...
-
16
FastAI.jl
Repository of best practices for deep learning in Julia
FastAI.jl is a Julia library for training state-of-the-art deep learning models. From loading datasets and creating data preprocessing pipelines to training, FastAI.jl takes the boilerplate out of deep learning projects. It equips you with reusable components for every part of your project while remaining customizable at every layer. FastAI.jl comes with support for common computer vision and tabular data learning tasks, with more to come. -
17
text-dedup
All-in-one text de-duplication
text-dedup is a Python library that enables efficient deduplication of large text corpora by using MinHash and other probabilistic techniques to detect near-duplicate content. This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. It supports Jaccard similarity thresholding, parallel execution, and flexible... -
18
DataGym.ai
Open source annotation and labeling tool for image and video assets
...AI-assisted annotation tools reduce manual labeling effort, give you more time to finetune ML models and speed up your go to market of new products. Accelerate your computer vision projects by cutting down data preparation time up to 50%. A machine learning model is only as good as its training data. DATAGYM is an end-to-end workbench to create, annotate, manage, and export the right training data for your computer vision models. Your image data can be imported into DATAGYM from your local machine, from any public image URL or directly from an AWS cloud S3 bucket. ... -
19
TexGen is a geometric textile modelling software package to be used for obtaining engineering properties of woven textiles and textile composites. Citing TexGen We would be grateful if you could acknowledge use of TexGen where appropriate and suggest using one of the following references: L P Brown and A C Long. "Modelling the geometry of textile reinforcements for composites: TexGen", Chapter 8 in "Composite reinforcements for optimum performance (Second Edition)", ed. P Boisse,...
-
20
Feathr
A scalable, unified data and AI engineering platform for enterprise
Feathr is a data and AI engineering platform that is widely used in production at LinkedIn for many years and was open sourced in 2022. It is currently a project under LF AI & Data Foundation. Define data and feature transformations based on raw data sources (batch and streaming) using Pythonic APIs. Register transformations by names and get transformed data(features) for various use cases including AI modeling, compliance, go-to-market and more. Share transformations and data(features) across team and company. Feathr is particularly useful in AI modeling where it automatically computes your feature transformations and joins them to your training data, using point-in-time-correct semantics to avoid data leakage, and supports materializing and deploying your features for use online in production. -
21
Flux3D.jl
3D computer vision library in Julia
Flux3D.jl is a 3D vision library, written completely in Julia. This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl. -
22
ML workspace
All-in-one web-based IDE specialized for machine learning
All-in-one web-based development environment for machine learning. The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. 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)... -
23
TensorWatch
Debugging, monitoring and visualization for Python Machine Learning
TensorWatch is an open source debugging and visualization platform created by Microsoft Research to support machine learning, deep learning, and reinforcement learning workflows. It enables developers to observe training behavior in real time through interactive visualizations, primarily within Jupyter Notebook environments. The tool treats most data interactions as streams, allowing flexible routing, storage, and visualization of metrics generated during model training. A distinctive... -
24
OpenFace
A state-of-the-art facial behavior analysis toolkit
OpenFace is an advanced facial behavior analysis toolkit intended for computer vision and machine learning researchers, those in the affective computing community, and those who are simply interested in creating interactive applications based on facial behavior analysis. The OpenFace toolkit is capable of performing several complex facial analysis tasks, including facial landmark detection, eye-gaze estimation, head pose estimation and facial action unit recognition. -
25
Facets
Visualizations for machine learning datasets
...Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. Explore Facets Overview and Facets Dive on the UCI Census Income dataset, used for predicting whether an individual’s income exceeds $50K/yr based on their census data. The census data contains features such as age, education level, and occupation for each individual. Overview gives users a quick understanding of the distribution of values across the features of their dataset(s). Uncover several uncommon and common issues such as unexpected feature values, missing feature values for a large number of observation, training/serving skew and train/test/validation set skew.