Deep Learning Fundamentals of Deep Learning for Beginners

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This book is for anyone who wants to learn deep learning from a mathematical perspective. In the first chapter you will get an introduction to the deep learning concepts, then in the second chapter you will understand and review some of the mathematical equations that will help you understand deep learning. After that, we will go delve further into the deep learning architectures, and after every chapter you will find review questions just to reinforce your understanding of the content. When reading this book, it's preferable to know any programming language, such as Python or R, and the basics of linear algebra.
This book contains illustrations and step-by-step explanations with bullet points andexercisesfor easy and enjoyable learning
Benefits of reading this book that you're not going to find anywhere else:
- INTRODUCTION TO DEEP LEARNING
- MATHEMATICS FOR DEEP LEARNING
- SINGLE AND MULTILAYER PERCEPTRON MODELS
- AUTOENCODERS, RESTRICTED BOLTZMANN MACHINES
- EXPERIMENTAL DESIGN AND HEURISTICS
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