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    Fully Managed MySQL, PostgreSQL, and SQL Server

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

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    Cut Data Warehouse Costs by 54%

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    OP Vault

    OP Vault

    Give ChatGPT long-term memory using the OP Stack

    OP Vault is an open-source system designed to give large language models long-term memory by enabling them to interact with a custom knowledge base built from user-provided documents. It combines a backend written in Go with a React frontend, allowing users to upload files such as PDFs, text documents, and books to create a searchable repository of information.
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    Jittor

    Jittor

    Jittor is a high-performance deep learning framework

    Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. ...
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  • 3
    TensorFlow Addons

    TensorFlow Addons

    Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

    TensorFlow Addons is a repository of contributions that conform to well-established API patterns but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast-moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the...
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  • 4
    YOLOX

    YOLOX

    YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5

    YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. Prepare your own dataset with images and labels first. For labeling images, you can use tools like Labelme or CVAT. One more thing worth noting is that you should also implement pull_item and load_anno method...
    Downloads: 9 This Week
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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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    PyTorch pretrained BigGAN

    PyTorch pretrained BigGAN

    PyTorch implementation of BigGAN with pretrained weights

    An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis. This PyTorch implementation of BigGAN is provided with the pretrained 128x128, 256x256 and 512x512 models by DeepMind.
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  • 6

    Darkbot

    The IRC's Talking Robot

    [ Please read https://sourceforge.net/p/darkbot/news/2014/01/darkbots-revitalization/ ] Darkbot is a portable IRC chat robot written in the C language that can be taught responses to user inquiries, and even have conversations with them. Darkbot was originally created by Jason Hamilton as an aid for help channels on Intenet Relay Chat.
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    Downloads: 2 This Week
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  • 7
    Dutch sentiment analysis engine

    Dutch sentiment analysis engine

    Een module om de sentiment van een stuk Nederlandse tekst to bepalen

    ...Momenteel is de module in alpha versie beschikbaar en een beta versie wordt verwacht in november 2012. Als u ons wilt helpen bij het ontwikkelen of testen van deze module, neem dan contact op met Incentro via +31[0]15 76 40 750 - of info {at} incentro.com.
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