Showing 3 open source projects for "fb2k-component"

View related business solutions
  • Gemini 3 and 200+ AI Models on One Platform Icon
    Gemini 3 and 200+ AI Models on One Platform

    Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

    Build generative AI apps with Vertex AI. Switch between models without switching platforms.
    Start Free
  • Earn up to 16% annual interest with Nexo. Icon
    Earn up to 16% annual interest with Nexo.

    Access competitive interest rates on your digital assets.

    Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.
    Get started with Nexo.
  • 1
    ml.js

    ml.js

    Machine learning tools in JavaScript

    This library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser. 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.
    Downloads: 9 This Week
    Last Update:
    See Project
  • 2
    GNNPCSAFT Web App

    GNNPCSAFT Web App

    Smart Thermodynamic Modeling with Graph Neural Networks

    The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. More info on github repository.
    Downloads: 7 This Week
    Last Update:
    See Project
  • 3
    fe4ml-zh

    fe4ml-zh

    Feature Engineering for Machine Learning

    ...The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that algorithms can use effectively. The project explains techniques for creating, selecting, and transforming features in ways that improve model accuracy and robustness. It also discusses the role of domain knowledge, data preprocessing, and statistical reasoning in building effective machine learning models.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB