Showing 7 open source projects for "start up"

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  • 1
    ZenML

    ZenML

    Build portable, production-ready MLOps pipelines

    A simple yet powerful open-source framework that scales your MLOps stack with your needs. Set up ZenML in a matter of minutes, and start with all the tools you already use. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. ...
    Downloads: 3 This Week
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  • 2
    Interpretable machine learning

    Interpretable machine learning

    Book about interpretable machine learning

    This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the...
    Downloads: 5 This Week
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  • 3
    DeepPavlov

    DeepPavlov

    A library for deep learning end-to-end dialog systems and chatbots

    DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual...
    Downloads: 0 This Week
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  • 4
    Horovod

    Horovod

    Distributed training framework for TensorFlow, Keras, PyTorch, etc.

    Start scaling your model training with just a few lines of Python code. Scale up to hundreds of GPUs with upwards of 90% scaling efficiency.
    Downloads: 5 This Week
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    Veeam Data Platform v13.1 - Get Your Free Trial

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  • 5
    Hello AI World

    Hello AI World

    Guide to deploying deep-learning inference networks

    Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras.
    Downloads: 3 This Week
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  • 6
    Semantic Segmentation in PyTorch

    Semantic Segmentation in PyTorch

    Semantic segmentation models, datasets & losses implemented in PyTorch

    ...Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero during training. Considered as the go-to scheduler for semantic segmentation. One Cycle learning rate, for a learning rate LR, we start from LR / 10 up to LR for 30% of the training time, and we scale down to LR / 25 for remaining time, the scaling is done in a cos annealing fashion (see Figure bellow), the momentum is also modified but in the opposite manner starting from 0.95 down to 0.85 and up to 0.95.
    Downloads: 0 This Week
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  • 7

    EducationalLCS

    eLCS - Educational Learning Classifier System

    ...This way users can start by examining the simplest version of the code and progress forward. This code is intended to be used as an educational tool, or as algorithmic code building blocks.
    Downloads: 0 This Week
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