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www.oranlooney.com | ||
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thenumb.at
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| | | | | [AI summary] This text provides an in-depth exploration of how functions can be treated as vectors, particularly in the context of signal and geometry processing. It discusses the representation of functions as infinite-dimensional vectors, the use of Fourier transforms in various domains (such as 1D, spherical, and mesh-based), and the application of linear algebra to functions for tasks like compression and smoothing. The text also touches on the mathematical foundations of these concepts, including the Laplace operator, eigenfunctions, and orthonormal bases. It concludes with a list of further reading topics and acknowledges the contributions of reviewers. | |
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oslandia.com
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| | | | | At Oslandia, we like working with Open Source tool projects and handling Open (geospatial) Data. In this article series, we will play with the OpenStreetMap (OSM) map and subsequent data. Here comes the seventh article of this series, dedicated to user classification using the power of machine learn | |
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janakiev.com
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| | | | | There are many ways to compare countries and cities and many measurements to choose from. We can see how they perform economically, or how their demographics differ, but what if we take a look at data available in OpenStreetMap? In this article, we explore just that with the help of a procedure called t-SNE. | |
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www.nicktasios.nl
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| | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the third, and last, post, | ||
