Showing 54 open source projects for "review"

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  • 1
    AWS Step Functions Data Science SDK

    AWS Step Functions Data Science SDK

    For building machine learning (ML) workflows and pipelines on AWS

    ...You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example notebooks. These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. ...
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  • 2
    The Deep Review

    The Deep Review

    A collaboratively written review paper on deep learning, genomics, etc

    This repository is home to the Deep Review, a review article on deep learning in precision medicine. The Deep Review is collaboratively written on GitHub using a tool called Manubot (see below). The project operates on an open contribution model, welcoming contributions from anyone. To see what's incoming, check the open pull requests. For project discussion and planning see the Issues.
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  • 3
    Gensim-data

    Gensim-data

    Data repository for pretrained NLP models and NLP corpora

    ...The collection includes corpora such as text8 and 20 Newsgroups alongside pretrained word-vector resources such as GloVe. Every dataset retains its own licensing terms, so users must review individual usage conditions.
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  • 4

    scripthea

    Scripthea is designed to streamline of crafting prompts for T2I gen.

    ...This modular approach allows users to experiment with various combinations, facilitating a more systematic exploration of visual styles and themes. Why Scripthea? - Systematically explore various artistic styles and themes - Efficiently manage and review large batches of generated images. - Gain deeper insights into the relationship between prompts and visual outputs.
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