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    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

    Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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    Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

    General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

    Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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  • 1
    Watermark Anything

    Watermark Anything

    Official implementation of Watermark Anything with Localized Messages

    Watermark Anything (WAM) is an advanced deep learning framework for embedding and detecting localized watermarks in digital images. Developed by Facebook Research, it provides a robust, flexible system that allows users to insert one or multiple watermarks within selected image regions while maintaining visual quality and recoverability. Unlike traditional watermarking methods that rely on uniform embedding, WAM supports spatially localized watermarks, enabling targeted protection of...
    Downloads: 3 This Week
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  • 2
    D4RL

    D4RL

    Collection of reference environments, offline reinforcement learning

    D4RL (Datasets for Deep Data-Driven Reinforcement Learning) is a benchmark suite focused on offline reinforcement learning — i.e., learning policies from fixed datasets rather than via online interaction with the environment. It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. Researchers can load a dataset for a given task (e.g., maze navigation, manipulation) and apply their algorithm...
    Downloads: 1 This Week
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  • 3
    Materials Discovery: GNoME

    Materials Discovery: GNoME

    AI discovers 520000 stable inorganic crystal structures for research

    Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the...
    Downloads: 0 This Week
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  • 4
    CC2.TV / CC2 - Audio- und TV-Datenbank

    CC2.TV / CC2 - Audio- und TV-Datenbank

    Meta-Datenbank-Anwendung für die Audio- und TV-Sendungen des CC2.TV

    ...Für die volle Funktionalität, insbesondere das Abspielen der Audiocasts, benötigen Sie ein Verzeichnis, in dem die MP3-Dateien des CC2.TV-Audiocasts gespeichert sind. Dieses Verzeichnis dient als Haupt-Installationsverzeichnis für die Anwendung. <Ihr Installationsverzeichnis>/ ├── CC-Zwei-000.mp3 ├── CC-Zwei-001.mp3 ├── ... └── CC-Zwei-XXX.mp3 Sie können die Anwendung auch in ein leeres Verzeichnis installieren.
    Downloads: 0 This Week
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    Fully Managed MySQL, PostgreSQL, and SQL Server

    Automatic backups, patching, replication, and failover. Focus on your app, not your database.

    Cloud SQL handles your database ops end to end, so you can focus on your app.
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  • 5
    torchtext

    torchtext

    Data loaders and abstractions for text and NLP

    We recommend Anaconda as a Python package management system. Please refer to pytorch.org for the details of PyTorch installation. LTS versions are distributed through a different channel than the other versioned releases. Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses. To build torchtext from source, you need git, CMake and C++11 compiler such as g++. When building from source, make sure that you have the same C++...
    Downloads: 1 This Week
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  • 6
    CC-Net

    CC-Net

    Tools to download and cleanup Common Crawl data

    cc_net provides tools to download, segment, clean, and filter Common Crawl to build large-scale text corpora, including monolingual datasets and the multilingual CC-100 collection introduced in the associated paper. It includes pipelines to fetch snapshots, extract text, de-duplicate, identify language, and apply quality filtering based on heuristics and language models. The outputs are intended for pretraining language models and for creating standardized corpora that can be reproduced or updated with new crawls. ...
    Downloads: 0 This Week
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