pytea VS backpack

Compare pytea vs backpack and see what are their differences.

backpack

BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient. (by f-dangel)
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pytea backpack
3 2
310 541
0.3% -
1.8 1.9
about 2 years ago 2 days ago
TypeScript Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

pytea

Posts with mentions or reviews of pytea. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

backpack

Posts with mentions or reviews of backpack. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-28.

What are some alternatives?

When comparing pytea and backpack you can also consider the following projects:

examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both

TorchDrift - Drift Detection for your PyTorch Models

uncertainty-toolbox - Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

SGD-OGR-Hessian-estimator - SGD (stochastic gradient descent) with OGR - online gradient regression Hessian estimator

WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

vivit - [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps

Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

pnotify - Beautiful JavaScript notifications with Web Notifications support.