https://github.com/tensorly/tensorly
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Tip revision: bfda61015d0817d259ec539d4fcf876e81a5ed1d authored by Jean Kossaifi on 14 April 2021, 11:14 UTC
Pypi Worflow: use correct token for test pypi
Tip revision: bfda610
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          <h1 class="hero-title title">
            <span class="hero-title-1">
              Fast & Simple 
            </span>
            <br/>
            <span class="hero-title-2">
                Tensor Learning 
            </span>
            <br>
            <span class="hero-title-1">
              In Python 
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          </h1>
          
          <div class="content tensorly-functionalities">
            <ul>
                <li> Open Source, BSD Licensed</li> 
                <li> Pure Python, Tested & Optimized </li>
                <li> Flexible Backends for:
                        <a target="_blank" href="https://numpy.org/"> NumPy</a>,
                        <a target="_blank" href="https://pytorch.org/"> PyTorch</a>,
                        <a target="_blank" href="https://www.tensorflow.org/"> TensorFlow</a>,
                        <a target="_blank" href="https://jax.readthedocs.io/en/latest/"> JAX</a>,
                        <a target="_blank" href="https://mxnet.apache.org/versions/1.7.0/"> Apache MXNet </a>
                        and <a target="_blank" href="https://cupy.dev/"> CuPy </a> </li>
                </li>
                <li> Thorough Documentation </li>
                <li> Minimal Dependencies </li>
                <li> Tensorized Deep Learning with <a href="http://tensorly.org/torch/dev/" target="_blank">TensorLy-Torch</a> </li>
            </ul>
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                Get started
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          <img src="_static/tensorly-pyramid.png" class="logo" alt="TensorLy logo">
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        <p class="title hero-discover-title">Discover TensorLy's Functionalities!</p>
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        <h3 class="title is-h3 ">Tensor Methods, Made Easy </h3>
        <div class="description-content is-size-5">
        <p> TensorLy provides all the utilities to easily use tensor methods, 
          whether you are an advanced user or just getting started, 
          from core tensor operations and tensor algebra to 
          tensor decomposition and regression.
        </p>
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      <div class="column is-one-quarter landing-functionalities">
        <h3 class="title is-h3"> Execute Anywhere </h3>
        <div class="description-content is-size-5">
        <p> TensorLy's backend system lets you write your code once and execute in using any of the supported frameworks, enabling tensor learning on GPU, multi-machines, and deep tensorized learning.
          <!-- The Tensor Algebra backend allows you to use our optimized implementations, dispatch all operations to einsum, or define your own dispatcher. -->
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        <h3 class="title is-h3">Speedup Your Research </h3>
        <div class="description-content is-size-5">
        <p> TensorLy is open-source, actively maintained and easily extensible.
          <!-- using the provided tools any tensor method you want can be quickly implemented. -->
            Its BSD license makes it suitable for use in both industry and academia and it is already used by several university labs and companies all around the world.</p>
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                    <p class="title">
                      <span class="icon is-large"><i class="fab fa-python"></i></span>
                      Install TensorLy
                    </p>
                    <p class="subtitle">Installation Instructions </p>
                </a>
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                      <span> User Guide </span>
                    </p>
                    <p class="subtitle">A Friendly Guide to Tensor Learning</p>
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                      Examples
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                    <p class="subtitle">See Usage Examples With Code</p>
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                        API
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                    <p class="subtitle">Functions and Classes Documentation</p>
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                      <span>Meet The Team</span>
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                    <p class="subtitle">About the Developers</p>
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                        <span>Contribute</span>
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                    <p class="subtitle">Source Code on Github</p>
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    <!-- CITE -->
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        <p>
          If you use TensorLy, please consider citing us:
        </p>
        <p>
          <it>Jean Kossaifi, Yannis Panagakis, Anima Anandkumar and Maja Pantic</it>, 
          <strong> TensorLy: Tensor Learning in Python</strong>, 
          Journal of Machine Learning Research, Year: 2019, Volume: 20, Issue: 26, Pages: 1−6.
          <br/> <a href="http://jmlr.org/papers/v20/18-277.html">http://jmlr.org/papers/v20/18-277.html</a>.
        </p>
        <blockquote id="bibtex" class="is-hidden">
            @article{tensorly, <br/>
             &emsp; author = {Jean Kossaifi and Yannis Panagakis and Anima Anandkumar and Maja Pantic}, <br/>
             &emsp; title = {TensorLy: Tensor Learning in Python}, <br/>
             &emsp; journal = {Journal of Machine Learning Research (JMLR)}, <br/>
             &emsp; volume = {20},<br/>
             &emsp; number = {26},<br/>
             &emsp; year = {2019}, <br/>
            } <br/>
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