Tag Archives: numpy

“Optimizing” blue noise dithering – backpropagation through Fourier transform and sorting

Introduction This will be a blog post that is second in an (unanticipated) series on interesting uses of the JAX numpy autodifferentiation library, as well as an extra post in my very old post series on dithering in games and … Continue reading

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Using JAX, numpy, and optimization techniques to improve separable image filters

In today’s blog post I will look at two topics: how to use JAX (“hyped” new Python ML / autodifferentiation library), and a basic application that is follow-up to my previous blog post on using SVD for low-rank approximations and … Continue reading

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Separate your filters! Separability, SVD and low-rank approximation of 2D image processing filters

In this blog post, I explore separable convolutional image filters: how can we check if a 2D filter is separable, and how to compute separable approximations to any arbitrary 2D filter represented in a numerical / matrix form using SVD. Continue reading

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Analyze your own activity data using Google Takeout – music listening stats example

The goal of this post is to show how to download our own data stored and used by internet services to generate personalized stats / charts like below and will show step-by-step how to do it using colab, Python, pandas, … Continue reading

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