A detailed derivation of Mean-Field Variational Bayes, its connection to Expectation-Maximization, and its implicit motivation for the "black-box variational inference" methods born in recent years.
Deriving the expectation-maximization algorithm, and the beginnings of its application to LDA. Once finished, its intimate connection to variational inference is apparent.
Stochastic maximum likelihood, contrastive divergence, negative contrastive estimation and negative sampling for improving or avoiding the computation of the gradient of the log-partition function. (Oof, that's a mouthful.)
A pedantic walk through Boltzmann machines, with focus on the computational thorn-in-side of the partition function.
Introducing the RBF kernel, and motivating its ubiquitous use in Gaussian processes.
A thorough, straightforward, un-intimidating introduction to Gaussian processes in NumPy.
Convolutional variational autoencoders for emoji generation and Siamese text-question-emoji-answer models. Keras, bidirectional LSTMs and snarky tweets @united within.
Coupling nimble probabilistic models with neural architectures in Edward and Keras: "what worked and what didn't," a conceptual overview of random effects, and directions for further exploration.
Exploring generative vs. discriminative models, and sampling and variational methods for approximate inference through the lens of Bayes' theorem.
Statistical underpinnings of the machine learning models we know and love. A walk through random variables, entropy, exponential family distributions, generalized linear models, maximum likelihood estimation, cross entropy, KL-divergence, maximum a posteriori estimation and going "fully Bayesian."