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thenumb.at | ||
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jingnanshi.com
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| | | | | Tutorial on automatic differentiation | |
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matbesancon.xyz
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| | | | | What can automated gradient computations bring to mathematical optimizers, what does it take to compute? | |
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jhui.github.io
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| | | | | [AI summary] The provided text discusses various mathematical and computational concepts relevant to deep learning, including poor conditioning in matrices, underflow/overflow in softmax functions, Jacobian and Hessian matrices, learning rate optimization using Taylor series, Newton's method, saddle points, constrained optimization with Lagrange multipliers, and KKT conditions. These concepts are crucial for understanding numerical stability, optimization algorithms, and solving constrained problems in machine learning. | |
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vitalyobukhov.wordpress.com
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| | | Visit the post for more. | ||
