It doesn't just matter what you have to say. How you say it is what matters. This holds true of information, mathematics but also coding. In this post, I cover best practices to make sure you are understood when you write Python code.

A retrospect on 2021 and looking ahead to 2022

An overview of the new version of TensorLy and the many new features and improvements it brings.

A potpourri of thoughts on designing the ideal tensor algebra and deep learning framework

Is work enough to reach true mastery?

Born again personal website - how to quickly refresh a blog

In version 0.2.0, TensorLy was refactored to support backends. As proof of concept I put together a PyTorch backend. It makes it trivial to combine pytorch code with tensor methods. In this post we demonstrate this by performing Tucker tensor decomposition using autograd and gradient descent.

From version 0.2.0, TensorLy has an MXNet Backend, in addition to the NumPy backend. This allows to perform tensor operations on multi-machines, on CPU and GPU seemlessly, as well as to integrate with Deep Neural Networks. This posts goes over this new version and how to install it.

A look at tensor unfolding and its different definitions. We go through their mathematical properties, and python implementation with NumPy.

A demonstration on how to use TensorLy to perform robust tensor Principal Component Analysis, with an application on images and videos.