Overview
- Provides recent advances in the fields of Deep Learning
- Presents theoretical advances and its applications to real-life problems
- Offers concepts and techniques of deep learning in a precise and clear manner
Part of the book series: Studies in Big Data (SBD, volume 91)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (15 chapters)
-
Front Matter
-
Theoretical Foundation of Deep Learning Theory and Analysis
-
Front Matter
-
-
Computing System and Machine Learning
-
Front Matter
-
-
Deep Learning Algorithms
-
Front Matter
-
-
Applications of Deep Learning Techniques
-
Front Matter
-
Keywords
- Deep Learning
- Deep Networks
- Machine Learning
- Computational Intelligence
- Deep Learning Algorithms
- Deep Learning Applications
- Deep Learning Concepts
- Supervised Learning
- Unsupervised Learning
- Kernel Learning
- Representation Learning
- Intelligent System
- Data Mining
- Knowledge Representation
- Knowledge
- Discovery Databases
- Image Processing
- Management Decision Making
About this book
This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.
Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Editors and Affiliations
About the editors
Bibliographic Information
Book Title: Deep Learning in Data Analytics
Book Subtitle: Recent Techniques, Practices and Applications
Editors: Debi Prasanna Acharjya, Anirban Mitra, Noor Zaman
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-030-75855-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-75854-7Published: 12 August 2021
Softcover ISBN: 978-3-030-75857-8Published: 13 August 2022
eBook ISBN: 978-3-030-75855-4Published: 11 August 2021
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XX, 266
Number of Illustrations: 22 b/w illustrations, 92 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Big Data, Data Mining and Knowledge Discovery