Mohamed Elgendy - Deep Learning for Vision Systems

Mohamed Elgendy

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ISBN: 9781617296192

To my mom, Huda, who taught me perseverance and kindness To my dad, Ali, who taught me patience and purpose To my loving and supportive wife, Amanda, who always inspires me to keep climbing To my two-year-old daughter, Emily, who teaches me every day that AI still has a long way to go to catch up with even the tiniest humans

Two years ago, I decided to write a book to teach deep learning for computer vision from an intuitive perspective. My goal was to develop a comprehensive resource that takes learners from knowing only the basics of machine learning to building advanced deep learning algorithms that they can apply to solve complex computer vision problems.

The problem : In short, as of this moment, there are no books out there that teach deep learning for computer vision the way I wanted to learn about it. As a beginner machine learning engineer, I wanted to read one book that would take me from point A to point Z. I planned to specialize in building modern computer vision applications, and I wished that I had a single resource that would teach me everything I needed to do two things: 1) use neural networks to build an end-to-end computer vision application, and 2) be comfortable reading and implementing research papers to stay up-to-date with the latest industry advancements.

I found myself jumping between online courses, blogs, papers, and YouTube videos to create a comprehensive curriculum for myself. Its challenging to try to comprehend what is happening under the hood on a deeper level: not just a basic understanding, but how the concepts and theories make sense mathematically. It was impossible to find one comprehensive resource that (horizontally) covered the most important topics that I needed to learn to work on complex computer vision applications while also diving deep enough (vertically) to help me understand the math that makes the magic work.

As a beginner, I searched but couldnt find anything to meet these needs. So now Ive written it. My goal has been to write a book that not only teaches the content I wanted when I was starting out, but also levels up your ability to learn on your own.

My solution is a comprehensive book that dives deep both horizontally and vertically:

• Horizontally --This book explains most topics that an engineer needs to learn to build production-ready computer vision applications, from neural networks and how they work to the different types of neural network architectures and how to train, evaluate, and tune the network.

• Vertically --The book dives a level or two deeper than the code and explains intuitively (and gently) how the math works under the hood, to empower you to be comfortable reading and implementing research papers or even inventing your own techniques.

At the time of writing, I believe this is the only deep learning for vision systems resource that is taught this way. Whether you are looking for a job as a computer vision engineer, want to gain a deeper understanding of advanced neural networks algorithms in computer vision, or want to build your product or startup, I wrote this book with you in mind. I hope you enjoy it.

This book was a lot of work. No, make that really a lot of work! But I hope you will find it valuable. There are quite a few people Id like to thank for helping me along the way.

I would like to thank the people at Manning who made this book possible: publisher Marjan Bace and everyone on the editorial and production teams, including Jennifer Stout, Tiffany Taylor, Lori Weidert, Katie Tennant, and many others who worked behind the scenes.

Many thanks go to the technical peer reviewers led by Alain Couniot--Al Krinker, Albert Choy, Alessandro Campeis, Bojan Djurkovic, Burhan ul haq, David Fombella Pombal, Ishan Khurana, Ita Cirovic Donev, Jason Coleman, Juan Gabriel Bono, Juan Jos Durillo Barrionuevo, Michele Adduci, Millad Dagdoni, Peter Hraber, Richard Vaughan, Rohit Agarwal, Tony Holdroyd, Tymoteusz Wolodzko, and Will Fuger--and the active readers who contributed their feedback in the book forums. Their contributions included catching typos, code errors and technical mistakes, as well as making valuable topic suggestions. Each pass through the review process and each piece of feedback implemented through the forum topics shaped and molded the final version of this book.

Finally, thank you to the entire Synapse Technology team. Youve created something thats incredibly cool. Thank you to Simanta Guatam, Aleksandr Patsekin, Jay Patel, and others for answering my questions and brainstorming ideas for the book.

If you know the basic machine learning framework, can hack around in Python, and want to learn how to build and train advanced, production-ready neural networks to solve complex computer vision problems, I wrote this book for you. The book was written for anyone with intermediate Python experience and basic machine learning understanding who wishes to explore training deep neural networks and learn to apply deep learning to solve computer vision problems.