Search Results for "ml"

The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks.

Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning. Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process. User management panel with role-based access control.

...The app is built on Next.js and exposes a simple web interface where users can upload images, choose styles, and view generated outputs. Under the hood, it calls a hosted ML model (for example on Replicate) via an API route and uses a service like Bytescale for image storage, keeping the front-end lightweight. The project is often described as an open-source clone or alternative to tools like InteriorAI, making AI-driven interior design experimentation accessible to developers. Because it is open source, developers can fork it, plug in different models, change the UI, or adapt the concept to other domains like garden layouts or product staging.

tvm-cn is a community-driven project that provides Chinese documentation for the Apache TVM deep learning compiler stack. Apache TVM is an open-source system designed to optimize and deploy machine learning models efficiently across different hardware platforms such as CPUs, GPUs, and ARM devices. The goal of the repository is to centralize translated learning materials and technical documentation so that Chinese-speaking developers can study the TVM ecosystem more easily. The project...

...Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Build and train models directly in JavaScript using flexible and intuitive APIs. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js.