Showing 4 open source projects for "deep learning toolbox"

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    tslab

    tslab

    Interactive JavaScript and TypeScript programming with Jupyter

    tslab is an interactive programming environment and REPL with Jupyter for JavaScript and TypeScript users. You can write and execute JavaScript and TypeScript interactively on browsers and save results as Jupyter notebooks.
    Downloads: 0 This Week
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    Shumai

    Shumai

    Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun

    Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. ...
    Downloads: 0 This Week
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  • 3
    Ignite UI CLI

    Ignite UI CLI

    Ignite UI Command-Line Interface by Infragistics

    A complete Angular components library of Angular-native UI controls and material-based UI components, including the fastest Angular data grid and 60+ high-performance charts! Compatible with Angular 16 and built to be enterprise-grade. Kickstart your next project with our drag-and-drop WYSIWYG App Builder for the fastest way to deliver high-performance Angular apps. Angular app development has never been easier with the cloud-based WYSIWYG drag & drop App Builder™. Instantly convert designs...
    Downloads: 0 This Week
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  • 4
    Codeball AI

    Codeball AI

    AI Code Review that finds bugs and fast-tracks your code

    ...Fully customizable and programmable with GitHub Actions. Codeball Actions are built on multiple smaller building blocks, that are heavily configurable through GitHub Actions. Codeball uses a deep learning model that has been trained on over 1 million Pull Requests. For each contribution, it considers hundreds of inputs. Codeball is optimized for precision, which means it only approves contributions that it's really confident in.
    Downloads: 0 This Week
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