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How the AI-Aided Engineering initiative advanced neural operators, geometry-aware learning, open source, and scientific machine learning in 2025.

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

Is work enough to reach true mastery?

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

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.

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