Revision 4d8452a1957b10716539bc14d7cfd10dd2c9a86a authored by TUNA Caglayan on 02 June 2021, 15:03:48 UTC, committed by TUNA Caglayan on 04 June 2021, 11:51:46 UTC
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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 
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            <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>
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                Get started
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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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        <h3 class="title is-h3"> Execute Anywhere </h3>
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        <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>
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        <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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                      <span class="icon is-large"><i class="fab fa-python"></i></span>
                      Install TensorLy
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                    <p class="subtitle">Installation Instructions </p>
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                      <span> User Guide </span>
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                    <p class="subtitle">A Friendly Guide to Tensor Learning</p>
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                      Examples
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                        API
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                      <span>Meet The Team</span>
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                        <span>Contribute</span>
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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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