cerbos VS Pytorch

Here, Cerbos comes into the picture.

In this tutorial you will learn how to use Cerbos to add fine-grained access control to any Nuxt web application, simplifying authorization as a result.

In my work with Cerbos, I apply the lessons learned from Da Vinci to tackle authorization challenges. Our approach is to create solutions where functionality seamlessly integrates with developer experience. Constantly iterating and viewing the tools through the users' lens, helps ensure that our access control solutions are robust and dev-friendly.

You may already know of our open source solution - Cerbos Policy Decision Point (PDP); a devtool which helps developers enforce access control over different parts of their software. If you need to learn more about Cerbos in general, we strongly recommend checking out the website and the docs.

GitHub: https://github.com/cerbos/cerbos URL: https://cerbos.dev

Cerbos Hub is a managed service offering for the open source authorization product, Cerbos.

Hello fellow devs! I'm with Cerbos (https://cerbos.dev/), a tool designed to manage who can do what in your software applications.

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():