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Image of - Artificial Neural Networks | Hardcover
Artificial Neural Networks | Hardcover

Artificial Neural Networks | Hardcover

by Da Silva

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

Highlights

  • binding-icon

    9783319431611

    ISBN:

  • binding-icon

    Da Silva

    Author:

  • binding-icon

    307

    Pages:

  • binding-icon

    215 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2017

    Year:

  • edition-icon

    1st Edition

    Edition:

  • binding-icon

    Hardcover

    Binding:

₹8891

₹11114

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

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