Showing 4 open source projects for "simple text editor"

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  • 1
    DeepDetect

    DeepDetect

    Deep Learning API and Server in C++14 support for Caffe, PyTorch

    ...While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. ...
    Downloads: 1 This Week
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  • 2
    AudioCraft

    AudioCraft

    Audiocraft is a library for audio processing and generation

    AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. ...
    Downloads: 1 This Week
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  • 3
    Deep Daze

    Deep Daze

    Simple command line tool for text to image generation

    Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text.
    Downloads: 0 This Week
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  • 4
    Meta-Learning-Papers

    Meta-Learning-Papers

    Meta Learning/Learning to Learn/One Shot Learning/Few Shot Learning

    Meta-Learning-Papers is a curated bibliography focused specifically on meta-learning, learning-to-learn, one-shot learning, and few-shot learning, intended for researchers and practitioners interested in this rapidly evolving subfield of machine learning. It catalogs foundational “legacy” papers that introduced key concepts, as well as more recent work that extends meta-learning to new domains or architectures. The list spans topics such as gradient-based meta-learning, metric-based and...
    Downloads: 0 This Week
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    PRTG Catches Network Issues Before They Cause Downtime

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