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| | hadrienj.github.io
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| | I'd like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua B...
| | nickhar.wordpress.com
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| | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B...
| | qchu.wordpress.com
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| | As a warm-up to the subject of this blog post, consider the problem of how to classify$latex n \times m$ matrices $latex M \in \mathbb{R}^{n \times m}$ up to change of basis in both the source ($latex \mathbb{R}^m$) and the target ($latex \mathbb{R}^n$). In other words, the problem is todescribe the equivalence classes of the...
| | terrytao.wordpress.com
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| Let $latex {G = (G,+)}&fg=000000$ be a finite additive group. A tiling pair is a pair of non-empty subsets $latex {A, B}&fg=000000$ such that every element of $latex {G}&fg=000000$ can