Understanding Deep Learning
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Understanding Deep Learning

Application in Rare Event Prediction

About the Book

Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the "how" transcends the creation.

Likewise, an understanding of the "how" transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified.

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    • Data Science
    • Machine Learning
    • Artificial Intelligence
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About the Author

Chitta Ranjan
Chitta Ranjan

Director of Science, ProcessMiner Inc.

Reader Testimonials

Zhen Zu
Zhen Zu

PhD Statistics, Stanford University

This is one of the best books out there in machine learning and data science. It's a good read for both practitioners and for people who want to go a little deeper. Super clear with great illustrations!

Jason Wu, Ph.D.
Jason Wu, Ph.D.

Partner, UltraBright Education.

This is an excellent textbook on Deep Learning with a focus on rare events. Chitta explains "what" the primary constructs of deep learning are with clear visual illustrations. I particularly enjoy the details of the motivation and intuitions on the "how' and "why" we make different choices in building deep learning models. I personally learned a tremendous amount and would recommend this book to anyone who wants to dig deeper into the subject.

Lingchao Mao
Lingchao Mao

PhD, Georgia Tech

Among the several Deep Learning courses and bootcamps I have had, Dr. Ranjan’s lectures and his book are one of the best Deep Learning resources. I have a background in engineering/statistics and I am often overwhelmed by the fast-evolving techniques in the Deep Learning field, inundated by “this model works very well” not knowing the why. Dr. Ranjan did an amazing job at pulling out the intuitions behind “why it works” and at connecting the dots of deep learning with concepts in traditional sta

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