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The Inner Workings
- of -
word2vec
By Chris McCormick
1
It is my earnest desire that the information in this book be as correct as
possible; however, I cannot make any guarantees. This is an evolving
book about an evolving technology in an evolving field--there are
going to be mistakes! So here’s my disclaimer: The author does not
assume and hereby disclaims any liability to any party for any loss,
damage, or disruption caused by errors or omissions, whether such
errors or omissions result from negligence, accident, or any other
cause.
Copyright © 2019 by Chris McCormick
All rights reserved.
Edition: v1.3.1
2
Contents
Introduction 4
1. Word Vectors & Their Applications 7
1.1. What’s a Word Vector? 8
1.2. Feature Vectors & Similarity Scores 9
1.3 Example Code Summary 11
2. Skip-gram Model Architecture 12
2.1. The Fake Task 13
2.2. Model Details 16
2.3. The Hidden Layer 17
2.4. The Output Layer 19
2.5. Intuition 20
2.6. Next Up 21
2.7. Example Code Summary 22
3. Sampling Techniques 23
3.1. Performance Problems 24
3.2. Subsampling Frequent Words 25
3.3. Context Position Weighting 28
3.4. Negative Sampling 30
3.5. Example Code Summary 33
4. Model Variations 33
4
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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