Why So Naive, Bayes?
I describe Naive Bayes, a commonly-used generative model for a variety of classification tasks.
Introduction to Logistic Regression
I describe logistic-regression, one of the cornerstone algorithms of the modern-day machine learning toolkit.
Fundamentals of Linear Regression
I describe the basics of linear regression, one of the most common and widely used machine learning techniques.
Transformers: Attention in Disguise
We discuss the Transformer, a purely attention-based architecture that is more performant, more efficient, and more parallelizable than recurrent network-based models.
Deep Contextualized Word Representations with ELMo
I describe ELMo, a recently released set of neural word representations that are pushing the state-of-the-art in natural language processing pretraining methodology.
Fundamental Deep Learning Algorithms To Learn
A discussion of fundamental deep learning algorithms people new to the field should learn along with a recommended course of study.
Why All The Excitement About Artificial Intelligence
Given all the recent buzz around artificial intelligence, I discuss three reasons for why we are seeing such widespread interest in the field today.
Being a Good Machine Learning Engineer/Data Scientist
A discussion of the most important skills necessary for being an effective machine learning engineer or data scientist.
Review of SIGDial/SemDial 2017
Following my attendance at the 18th Annual Meeting on Discourse and Dialogue, I summarize the most promising directions for future dialogue research, as gleaned from discussions with other researchers at the conference.