Showing 3 open source projects for "deep neural network"

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    Multi-cloud security delivered — now and in the future.

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    pH7 Social Dating CMS (pH7Builder)❤️

    pH7 Social Dating CMS (pH7Builder)❤️

    🚀 Professional Social Dating Web App Builder (formerly pH7CMS)

    pH7Builder is a Professional, Free & Open Source PHP Social Dating Builder Software (primarily designed for developers ...). This Social Dating Web App is fully coded in object-oriented PHP (OOP) with the MVC pattern (Model-View-Controller). It is low resource-intensive, extremely powerful and highly secure. pH7Builder is included with over 42 native modules and is based on its homemade pH7 Framework which includes more than 52 packages To summarize, pH7Builder Social Dating Script...
    Downloads: 26 This Week
    Last Update:
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  • 2
    CrypTen

    CrypTen

    A framework for Privacy Preserving Machine Learning

    ...Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. Its design mirrors PyTorch’s modular and library-based structure, enabling flexible experimentation, debugging, and model development. The framework supports both encryption and decryption of tensors and operations such as addition and multiplication over encrypted values. Although not yet production-ready, CrypTen focuses on advancing real-world secure ML applications, such as training and inference over private datasets, without exposing sensitive data.
    Downloads: 0 This Week
    Last Update:
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  • 3
    TenorSpace.js

    TenorSpace.js

    Neural network 3D visualization framework

    TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information.
    Downloads: 1 This Week
    Last Update:
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