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.

The package is based around `torus mapping', which is a non-perturbative technique for creating orbital tori for specified values of the action integrals. Given an orbital torus and a star's position at a reference time, one can compute its position at any other time, no matter how remote.

Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI:10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)<DOI:10.1080/02664763.2020.1864817>." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). <DOI:10.1214/aoms/1177704374>. "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model.

Draws tornado plots for model sensitivity to univariate changes. Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package. Also draws variable importance plots.

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>.

Documentation at https://melpa.org/#/torus

This package 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.

This package 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.

This package implements additional operators for computer vision models, including operators necessary for image segmentation and object detection deep learning models.

Documentation at https://melpa.org/#/torrent-mode

image and video datasets and models for torch deep learning

Tortoise ORM is an easy-to-use asyncio ORM (Object Relational Mapper) inspired by Django. Tortoise ORM was built with relations in mind and admiration for the excellent and popular Django ORM. It's engraved in its design that you are working not with just tables, you work with relational data.

Tortoise ORM is an easy-to-use asyncio ORM (Object Relational Mapper) inspired by Django. Tortoise ORM was built with relations in mind and admiration for the excellent and popular Django ORM. It's engraved in its design that you are working not with just tables, you work with relational data.

Tortoise ORM is an easy-to-use asyncio ORM (Object Relational Mapper) inspired by Django. Tortoise ORM was built with relations in mind and admiration for the excellent and popular Django ORM. It's engraved in its design that you are working not with just tables, you work with relational data.

This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost. For usage of ODE solvers in deep learning applications.

As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU.

Pypika-tortoise is a fork of pypika which has been streamlined for its use in the context of tortoise-orm. It removes support for many database kinds that tortoise-orm doesn't need, for example.

Pypika-tortoise is a fork of pypika which has been streamlined for its use in the context of tortoise-orm. It removes support for many database kinds that tortoise-orm doesn't need, for example.

Documentation at https://melpa.org/#/tornado-template-mode