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nla-group.org | ||
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opguides.info
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| | | | | 6 - Matrix Theory / Linear Algebra # Below is a 15 video series that totals a bit under 3 hours. Interactive Linear Algebra, text book that actually uses the web Linear Algebra Done Wrong - Sergei Treil @ Brown University Matrices, Diagrammatically Linear Algebra - Jim Hefferson Linear Algebra and Applications: An Inquiry-Based Approach | |
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nhigham.com
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| | | | | Backward error is a measure of error associated with an approximate solution to a problem. Whereas the forward error is the distance between the approximate and true solutions, the backward error is how much the data must be perturbed to produce the approximate solution. For a function $latex f$ from $latex \mathbb{R}^n$ to $latex \mathbb{R}^n$ | |
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francisbach.com
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| | | | | [AI summary] The blog post discusses non-convex quadratic optimization problems and their solutions, including the use of strong duality, semidefinite programming (SDP) relaxations, and efficient algorithms. It highlights the importance of these problems in machine learning and optimization, particularly for non-convex problems where strong duality holds. The post also mentions the equivalence between certain non-convex problems and their convex relaxations, such as SDP, and provides examples of when these relaxations are tight or not. Key concepts include the role of eigenvalues in quadratic optimization, the use of Lagrange multipliers, and the application of methods like Newton-Raphson for solving these problems. The author also acknowledges contributions... | |
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dagshub.com
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| | | Examine how you can improve the overall accuracy of your machine learning models so that they perform well and make reliable predictions. | ||
