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Advanced Deep Learning for Engineers and Scientists [electronic resource] : A Practical Approach / edited by Kolla Bhanu Prakash, Ramani Kannan, S.Albert Alexander, G. R. Kanagachidambaresan.

Contributor(s): Prakash, Kolla Bhanu [editor.] | Kannan, Ramani [editor.] | Alexander, S.Albert [editor.] | Kanagachidambaresan, G. R [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: EAI/Springer Innovations in Communication and Computing: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: XVII, 285 p. 281 illus., 261 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030665197.Subject(s): Telecommunication | Computational intelligence | Machine learning | Data mining | Communications Engineering, Networks | Computational Intelligence | Machine Learning | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- Introduction to ANN -- Introduction to Deep Learning -- Deep Soft Computing using Python -- Working with Keras -- Deep learning Applications using Python -- Advanced Deep learning techniques -- Conclusion.
In: Springer Nature eBookSummary: This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.
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Introduction -- Introduction to ANN -- Introduction to Deep Learning -- Deep Soft Computing using Python -- Working with Keras -- Deep learning Applications using Python -- Advanced Deep learning techniques -- Conclusion.

This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.

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