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.

Fast Object Detection with Fast R-CNN

I dive into the details of Fast R-CNN, an extension to the original R-CNN model that boasted 9x speedup over its predecessor as well as state-of-the-art object detection results.

Object Detection with R-CNN

A discussion of R-CNN, a historic object detection architecture combining region proposals with convolutional neural networks.

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.

A Software Interview Challenge

In light of a particularly exhausting software job interview cycle, I discuss the general format of such interviews and propose a playful challenge for any people going through a similar experience.

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.

A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset

To help spur conversational assistant research, we release a corpus of 3,031 grounded, multi-turn dialogues in three distinct domains appropriate for an in-car assistant: calendar scheduling, weather information retrieval, and point-of-interest navigation.