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terrytao.wordpress.com | ||
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almostsuremath.com
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| | | | | According to Kolmogorov's axioms, to define a probability space we start with a set ? and an event space consisting of a sigma-algebra F? on ?. A probability measure ? on this gives the probability space (?,?F?,??), on which we can define random variables as measurable maps from ? to the reals or other measurable... | |
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matthewmcateer.me
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| | | | | Important mathematical prerequisites for getting into Machine Learning, Deep Learning, or any of the other space | |
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francisbach.com
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| | | | | [AI summary] The blog post discusses the spectral properties of kernel matrices, focusing on the analysis of eigenvalues and their estimation using tools like the matrix Bernstein inequality. It also covers the estimation of the number of integer vectors with a given L1 norm and the relationship between these counts and combinatorial structures. The post includes a detailed derivation of bounds for the difference between true and estimated eigenvalues, highlighting the role of the degrees of freedom and the impact of regularization in kernel methods. Additionally, it touches on the importance of spectral analysis in machine learning and its applications in various domains. | |
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fodsi.us
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| | | [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. | ||
