Yasin Kutuk
Activation Functions: Activation Functions in Deep Learning with LaTeX Applications
Activation Functions: Activation Functions in Deep Learning with LaTeX Applications
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- More about Activation Functions: Activation Functions in Deep Learning with LaTeX Applications
This book provides a comprehensive explanation of 37 activation functions used in deep neural networks, along with their mathematical and visual representations, and offers LaTeX implementations for their common use in scientific articles.
Format: Paperback / softback
Length: 84 pages
Publication date: 31 May 2022
Publisher: Peter Lang AG
This book is a comprehensive guide to the functions commonly employed in deep neural networks. It aims to provide a deep understanding of these activation functions, both mathematically and visually. To facilitate this learning process, the book offers detailed explanations of 37 activation functions, along with their corresponding LaTeX implementations, which are widely used in scientific articles.
The first chapter of the book introduces the concept of activation functions and their role in neural networks. It explains how these functions transform the output of the neural network layers and contribute to the overall behavior of the network. The chapter also discusses the different types of activation functions, including sigmoid, tanh, ReLU, and Leaky ReLU, and their advantages and disadvantages.
In the subsequent chapters, each activation function is explored in detail. Mathematical equations and visual representations are used to illustrate the principles and characteristics of each function. The book also provides practical examples and applications of these activation functions in various deep learning tasks, such as image classification, speech recognition, and natural language processing.
To enhance the learning experience, the book includes comprehensive LaTeX implementations of all the activation functions discussed. This allows readers to directly copy and use the code in their own research or projects. Additionally, the book includes a glossary of technical terms and a reference list for further reading.
Whether you are a beginner or an experienced deep learning practitioner, this book will provide you with a solid foundation in activation functions and their applications. It will help you build more effective and efficient deep neural networks and advance your understanding of the field.
Weight: 123g
Dimension: 210 x 148 (mm)
ISBN-13: 9783631873281
Edition number: New ed
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