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.

Motivation, logistics and strategic insight re: designing the Open-Source "Master's" for yourself.

I'm joining ASAPP as a Machine Learning Engineer.

Beginning the search for an impossibly awesome next role.

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.