• Title: Neural Network Architecture Design
• Author(s) Alireza M. Javid
• Publisher: KTH Royal Institute of Technology (2021)
• Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-91-7873-773-4
• Share This:  

Book Description

This book focus on developing new neural network architectures while taking such practical constraints into account, provides a clear and detailed coverage of fundamental neural network architectures and learning rules.

• N/A

• Amazon

• Neural Networks
• Deep Learning
• Artificial Intelligence, Machine Learning, and Logic Programming
• Algorithms and Data Structures

• Neural Network Architecture Design (Alireza M. Javid)
• The Nonlinearity Coefficient - A Practical Guide to Neural Architecture Design

• Neural Network Architectures and Activation Functions

  Determining the right architecture is a computationally intensive process. We show that the neural activation function, if allowed to individually change for each neuron, can implicitly control many aspects of the network architecture.

• Neural Network Design (Martin T. Hagan)

  This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. It emphasizes a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.

• An Introduction to Neural Networks (Kevin Gurney)

  With an easy to understand format using graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics.

• Neural Network Learning: Theoretical Foundations

  This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions.

• Neural Networks and Deep Learning (Michael Nielsen)

  Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

• Applied Artificial Neural Networks (Christian Dawson)

  This book focuses on the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

• Neural Networks - A Systematic Introduction (Raul Rojas)

  In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. It is aimed at readers who seek an overview of the field or who wish to deepen their knowledge.

• Neural Networks (Rolf Pfeifer, et al)

  Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It is a systematic introduction to neural networks, biological foundation.