Deep Learning in Data Analytics

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.

• 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

• School of Computing Science and Engineering, VIT University, Vellore, India

  Debi Prasanna Acharjya

• Department of Computer Science Engineering, Amity University Kolkata, Kolkata, India

  Anirban Mitra

• School of Computer Science and Engineering, Taylor's University, Subang Jaya, Malaysia

  Noor Zaman

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