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www.jeremykun.com | ||
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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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jeremykun.wordpress.com
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| | | | | This post is a sequel toFormulating the Support Vector Machine Optimization Problem. The Karush-Kuhn-Tucker theorem Generic optimization problems are hard to solve efficiently. However, optimization problems whose objective and constraints have special structureoften succumb to analytic simplifications. For example, if you want to optimize a linear function subject to linear equality constraints, one can compute... | |
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thenumb.at
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| | | | | [AI summary] This text provides a comprehensive overview of differentiable programming, focusing on its application in machine learning and image processing. It explains the fundamentals of automatic differentiation, including forward and backward passes, and demonstrates how to implement these concepts in a custom framework. The text also discusses higher-order differentiation and its implementation in frameworks like JAX and PyTorch. A practical example is given using differentiable programming to de-blur an image, showcasing how optimization techniques like gradient descent can be applied to solve real-world problems. The text emphasizes the importance of differentiable programming in enabling efficient and flexible computation for various domains, includ... | |
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vankessel.io
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| | | A blog for my thoughts. Mostly philosophy, math, and programming. | ||
