Random/incomplete thoughts on various ML topics A Few Notes on Attention Models A Few Notes on Fisher Information (WIP) Some Notes on Kolmogorov Complexity, the Algorithmic Markov Condition, and Causality Causal Graphs and Sources of Bias Likelihood Ratio Policy Gradients for Reinforcement Learning Notes on Policy Gradients (Trust Region Policy Optimization, under construction) Notes on Policy Gradients (under construction) Notes on Noise Contrastive Estimation (NCE) Notes on Maximization of Inner Products over Norm Balls Notes on Adversarial Examples Basic probability stuff that everyone likely should know Notes on Variational Autoencoders Notes on policy gradient and the log derivative trick for reinforcement learning (under construction) LDA Intro/overview (also under construction) Brief intro to auto-encoders (I would explain this differently/use different notation these days) Pretty old random idea How exactly does word2vec work? Why is minimizing error the same thing as maximizing likelihood is the same thing as finding a low energy state... A bit on sequence to sequence learning (e.g Google Inbox smart reply) Code Snippets VariationalAutoencoder (VAE) and other examples written in tensorflow. An example reconstruction of MNIST digits by the VAE is shown below. . lr.py: Very simple linear regression in tensorflow (made up dataset) k-prototypes.py: Categorical Clustering for Netflow data Softmax regression on MNIST in tensorflow PCA + K-Means (Spark/MLlib/Scala) on the KDDCUP99 data set Last Update: 11.06.2022 by dmm613@gmail.com