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www.jeremykun.com | ||
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greydanus.github.io
A blog by Sam Greydanus |
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jeremykun.wordpress.com
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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jhui.github.io
[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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coornail.net
Neural networks are a powerful tool in machine learning that can be trained to perform a wide range of tasks, from image classification to natural language processing. In this blog post, well explore how to teach a neural network to add together two numbers. You can also think about this article as a tutorial for tensorflow. |
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