The goal of tor (to-R) is to help you to import multiple
    files from a single directory at once, and to do so as quickly,
    flexibly, and simply as possible.

Provides functionality to define and train neural networks similar to
    'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R
    using the 'libtorch' library. Also supports low-level tensor operations and
    'GPU' acceleration.

Provides datasets in a format that can be easily consumed by torch 'dataloaders'.
  Handles data downloading from multiple sources, caching and pre-processing so
  users can focus only on their model implementations.

Torch code for computing multi-class Area Under The Minimum,
 <https://www.jmlr.org/papers/v24/21-0751.html>, Generalization.
 Useful for optimizing Area under the curve.

Optimizers for 'torch' deep learning library. These
    functions include recent results published in the literature and are
    not part of the optimizers offered in 'torch'. Prospective users
    should test these optimizers with their data, since performance
    depends on the specific problem being solved.  The packages includes
    the following optimizers: (a) 'adabelief' by Zhuang et al (2020),
    <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019),
    <arXiv:1902.09843>; (c) 'adahessian' by Yao et al.(2021)
    <arXiv:2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019),
    <arXiv:1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021),
    <arXiv:2101.11075>; (f) 'nadam' by Dozat (2019),
    <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by
    Ma and Yarats(2019), <arXiv:1810.06801>; (h) 'radam' by Liu et al.
    (2019), <arXiv:1908.03265>; (i) 'swats' by Shekar and Sochee (2018),
    <arXiv:1712.07628>; (j) 'yogi' by Zaheer et al.(2019),
    <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.

Provides access to datasets, models and preprocessing
    facilities for deep learning with images. Integrates seamlessly
    with the 'torch' package and it's 'API' borrows heavily from
    'PyTorch' vision package.