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Links 1 through 10 of 49 by Juan Manuel Caicedo tagged reference

Cheat sheet or minitutorial about linear algebra.

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A concise reference on advanced matrix theory, including:
- An easy way to compute matrix derivatives and second derivatives
- A general framework for inverting partitioned matrices
- Useful properties of Kronecker product, Hadamard product, and diag
- The column-stacking operator "vec" and its generalization to "vec-transpose"

Includes applications to multilinear models, principal component analysis, blind source separation, Lyapunov equations, model alignment, and more.

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An overview of the methods available for performing operations on Numpy arrays, such as creating, indexing and slicing them.

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The purpose of this section of the ACL wiki is to be a repository of k-best state-of-the-art results (i.e., methods and software) for various core natural language processing tasks.

As a side effect, this should hopefully evolve into a knowledge base of standard evaluation methods and datasets for various tasks, as well as encourage more effort into reproducibility of results. This will help newcomers to a field appreciate what has been done so far and what the main tasks are, and will help keep active researchers informed on fields other than their specific research. The next time you need a system for PP attachment, or wonder what is the current state of word sense disambiguation, this will be the place to visit.

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This page contains lecture notes for the algorithms classes required for all computer science undergraduate and graduate students at the University of Illinois, Urbana-Champaign.

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Detexify is a service that converts hand-drawn math symbols into the corresponding LaTeX code.

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This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.

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A short list of video lectures of computer science courses taught at Stanford, including Machine Learning, Analysis of Algorithms and Discrete Probability.

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Compatibility tables for support of HTML5, CSS3, SVG and more in desktop and mobile browsers.

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A PNG version of the Machine Learning cheat sheet bookmarked previously.

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