Название: Understanding Deep Learning: Application in Rare Event Prediction
Автор: Chitta Ranjan
Издательство: Leanpub
Год: 2022
Страниц: 428
Язык: английский
Формат: pdf (true)
Размер: 11.4 MB

"It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger.

I hope every reader enjoys this voyage in Deep Learning and find their adventure.

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.

This book is appropriate for graduate and Ph.D. students, as well as researchers and practitioners. To the practitioners, the book provides complete illustrative implementations to use in developing solutions. For researchers, the book has several research directions and implementations of new ideas, e.g., custom activations, regularizations, and multiple pooling. In graduate programs, this book is suitable for a one-semester introductory course in deep learning. The first three chapters introduce the field, a working example, and sets up a student with TensorFlow.

The rest of the chapters present the deep learning constructs and the concepts therein. These chapters contain exercises. Most of them illustrate concepts in the respective chapter. Their practice is encouraged to develop a stronger understanding of the subject. Besides, a few advanced exercises are marked as optional. These exercises could lead a reader to develop novel ideas.

Additionally, the book illustrates how to implement Deep Learning networks with TensorFlow in Python. The illustrations are kept verbose and, mostly, verbatim for readers to be able to use them directly. The link to the code repository is also available at GitHub.




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