Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python

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Surekha Paneerselvam, PhD (Author) – Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa  Vidyapeetham, India
S. Sumathi, PhD (Author) – Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India
L. Ashok Kumar, PhD (Author) – Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India
Suresh Rajappa, PhD (Author) – Enterprise Solutions, KPMG LLP, Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India

Series: Computer Science, Technology and Applications
BISAC: COM094000; COM021030
DOI: https://doi.org/10.52305/XSMY1504

Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples.

The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalize these strategies. The book will benefit information professionals, programmers, consultants, professors, students, and industry experts who seek a variety of real-world illustrations with an implementation based on machine learning algorithms.

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Description

Preface

Acknowledgements

Chapter 1. Introduction

Chapter 2. Deep Learning

Chapter 3. Convolutional Neural Networks

Chapter 4. Recurrent Neural Networks

Chapter 5. Ensemble Learning

Chapter 6. Implementing DL and Ensemble Learning Models: Real World Use Cases

Appendix

Suggested Reading

About the Authors

Index

Additional information

Binding

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