Probability Theory and Random variables
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This Blog post briefly introduces some concepts in set theory and measure theory that are needed to define probability. It also talks about measurable transformations and random variables.
Blog posts
2020
Geometric view of matrices, Diagonalization and SVD
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I’ve found very few notes on the internet which talks about geometric intuition about matrices and linear algebra and wanted to write about if I ever found any good material. This blogpost talks about linear transformation, geometric intuitions of matrices, change of basis, eigen decomposition and singular value decomposition.
Variational Auto Encoder
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This blogpost talks about modelling the distribution of images, the challenges in modelling and training etc. Let’s try to fit a model \(p(x)\) to a set of images, just like we tried to fit GMM to a set of points. If you are not familiar with latent variable models and GMM you can refer to my previous post here
Bayesian view and Variational Inference
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This blog post talks about Bayesian view of statistics and the need for variatinal inference and a simple Mean Field approximation method
Latent Variables and EM
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This blog post talks about latent variables, why we need them and how to train latent variable models.