In 2012 I received my PhD in experimental condensed matter physics from West Virginia University. Following that I was a dry etch process engineer for Intel Corporation, where I leveraged big data to make essential process improvements for mission critical products. After leaving Intel in 2015, I have worked as a contract and freelance deep learning and artificial intelligence engineer.

Natural Language Processing from First Principles

Ready?

Natural Language Processing from First Principles

What You Will Learn in this Course

Required Background, Software, and Hardware

How to Succeed in this Course

What is the Purpose of Language?

How do we Represent Words with Computers?

Representing Words and Meaning with Vectors

Intuition of the Word2Vec Algorithm

Coding up Our Data Parser

Augmenting our Dataset and Implementing Sub-sampling

It's all about the Context

Overview of the Mathematics We Will Need

Essential Mathematical Functions

Coming to Grips with Optimization: A Crash Course in Calculus

Gradients of More Complicated Functions

Deriving the Word2Vec Gradients

The Skipgram Algorithm

Stochastic Gradient Descent

Turbocharging Skipgram with Negative Sampling

The Negative Sampling Loss Function and Gradients

Coding the Main Loop and Visualizing Our Word Vectors

A Primer on Reading Deep Learning Research Papers

Reading the Abstract and Introduction

Introducing the Skipgram Model

Empirical Results and Learning Phrases

Additive Compositionality, Comparisons, and Conclusion

About the teacher

phil