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Introduction
Welcome to my word2vec eBook! Whether you are a student learning
important machine learning concepts, a researcher exploring new
techniques and ideas, or an engineer with a vision to build a new
product or feature, my hope is that the content in this guide will help
you gain a deeper understanding of the algorithm, and equip you to
realize your own goals faster and with better results.
Here is an overview of the content you’ll find in this book.
Chapter 1 - Word Vectors & Their Applications
● This chapter will answer the questions, “what is a word vector?”
and “how are they useful?” I’ll explain how word vectors can be
used to measure how similar two words are in meaning, and the
value this has across a number of applications. You may skip this
section if you are already familiar with the motivations and uses
for word vectors.
Chapter 2 - Skip-gram Model Architecture
● After learning why word vectors are valuable, Chapter 2 will
address how (both conceptually and in implementation) the
word2vec approach is able to learn and encode the meaning of a
word.
Chapter 3 - Sampling Techniques
● The architecture described in chapter 2 is good in concept but
prohibitively expensive in practice. Negative Sampling is a slight
modification to the training process which is both dramatically
faster and produces higher quality results.
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