One of my favorite things about deep reinforcement learning is that, unlike supervised learning, it really, really doesn’t want to work. Throwing a neural net at a computer vision problem might get you 80% of the way there. …

This article is an updated comprehensive overview of Topic Modeling and its associated techniques. In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning — from words to sentences to paragraphs to documents. At the document level, one of the most useful…

With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around…

Over the past few years, much of the progress in deep learning for computer vision can be boiled down to just a handful of neural network architectures. …

Before I started my most recent job at ThinkTopic, the concepts of “functional programming” and “machine learning” belonged to two different worlds entirely. One was a programming paradigm surging in popularity as the world turned towards simplicity, composability, and immutability to maintain complex scaling applications; the other was a tool…

I don’t have anything against plays, per se. I’m just as excited for Harry Potter and the Cursed Child as the next millennial who grew up staring out their window at night, waiting for their letter from Hogwarts, only to get screwed over by what can only be assumed was…