Day: September 15, 2020

Gaussian samples – Part (1)

Background Gaussian sampling — that is, generating samples from a Gaussian distribution — plays an important role in many cutting-edge fields of data science, such as Gaussian process, variational autoencoder, or generative adversarial network. As a result, you often see functions like tf.random.normal in their tutorials. But, deep down, how does computer know how to generate Gaussian samples? This series …

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Duality theorems

Introduction Optimization shows up everywhere in machine learning, from the ubiquitous gradient descent to quadratic programming in SVM, to expectation-maximization algorithm in Gaussian mixture models. However, one aspect of optimization that always puzzled me is duality: what on earth are a primal form and dual form of an optimization problem, and what good do they serve? Therefore, in this project, I will: Go …

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