• $300 in Free Credit for Your Google Cloud Projects Icon
    $300 in Free Credit for Your Google Cloud Projects

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    Build AI Apps with Gemini 3 on Vertex AI

    Access Google’s most capable multimodal models. Train, test, and deploy AI with 200+ foundation models on one platform.

    Vertex AI gives developers access to Gemini 3—Google’s most advanced reasoning and coding model—plus 200+ foundation models including Claude, Llama, and Gemma. Build generative AI apps with Vertex AI Studio, customize with fine-tuning, and deploy to production with enterprise-grade MLOps. New customers get $300 in free credits.
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  • 1
    PyTorch Geometric Temporal

    PyTorch Geometric Temporal

    Spatiotemporal Signal Processing with Neural Machine Learning Models

    ...The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy - see the accompanying tutorial. ...
    Downloads: 0 This Week
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  • 2
    Rhino

    Rhino

    On-device Speech-to-Intent engine powered by deep learning

    ...Create use-case-specific voice AI models in seconds. Develop voice features with a few lines of code using intuitive and cross-platform SDKs. Deliver voice AI everywhere: on-device, mobile, web browsers, on-premise, or cloud. Measure adoption, learn, and iterate. Continuously re-design and re-train to optimize engagement. Building accurate, responsive, and private voice technology is difficult. We learned the hard way, so you don’t have to. Picovoice heavily invests in R&D to offer superior voice AI that surpasses even Big Tech in accuracy and efficiency. ...
    Downloads: 0 This Week
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  • 3
    AI-Agent-Host

    AI-Agent-Host

    The AI Agent Host is a module-based development environment.

    The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI...
    Downloads: 0 This Week
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  • 4
    OmicSelector

    OmicSelector

    Feature selection and deep learning modeling for omic biomarker study

    OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models.
    Downloads: 0 This Week
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    99.99% Uptime for MySQL and PostgreSQL on Google Cloud

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  • 5

    Face Recognition

    World's simplest facial recognition api for Python & the command line

    Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a...
    Downloads: 6 This Week
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  • 6
    TensorFlow World

    TensorFlow World

    Simple and ready-to-use tutorials for TensorFlow

    ...TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model.
    Downloads: 0 This Week
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