Foreword

I first met Mike Bernico when we were two of the founding members of a new data science team at a Fortune 50 company. Then, it was a heady time; there wasn't such a thing as formal data science education, so we were all self-taught. We were a collection of adventurous people from diverse backgrounds, who identified and learned data science techniques because we needed them to solve the problems that we were interested in. We built a team with an optimistic hacker approach—the belief that we could find and apply techniques "from the wild" to build interesting, useful things.

It is in this practical, scrappy spirit that Mike wrote Deep Learning Quick Reference book. Deep learning is frequently made out to be mysterious and difficult; however, in this guide, Mike breaks down major deep learning techniques, making them approachable and applicable. With this book, you (yes, you!) can quickly get started with using deep learning for your own projects in a variety of different modalities.

Mike has been practising data science since before the discipline was named, and he has been specifically teaching the topic to university students for 3 years. Prior to this, he spent many years as a working computer scientist with a specialization in networks and security, and he also has a knack for engaging with people and communicating with nonspecialists. He is currently the Lead Data Scientist for a large financial services company, where he designs systems for data science, builds machine learning models with direct applications and for research publications, mentors junior data scientists, and teaches stakeholders about data science. He knows his stuff!

With Deep Learning Quick Reference book, you'll benefit from Mike's deep experience, humor, and down-to-earth manner as you build example networks alongside him. After you complete Mike's book, you'll have the confidence and knowledge to understand and apply deep learning to the problems of your own devising, for both fun and function.

Bon voyage, and good hacking!

- J. Malia Andrus, Ph.D.

Data Scientist
Seattle Washington