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liorsinai.github.io | ||
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marcospereira.me
In this post we summarize the math behind deep learning and implement a simple network that achieves 85% accuracy classifying digits from the MNIST dataset. |
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yetanothermathprogrammingconsultant.blogspot.com
When using \({\bf diag}(x)\) in a text, there is always the nagging feeling that there must be a nice way to express this in standard matrix... |
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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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fodsi.us
[AI summary] The ML4A Virtual Workshop explores how machine learning enhances classical algorithms through data-driven approaches, featuring talks on deep generative models, model-based deep learning, and learning-augmented algorithms. |
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