Machine Learning Fundamentals Theory Algorithms And Real World Applications
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Machine Learning Fundamentals: Theory, Algorithms and Real-World Applications
Author: Dr.V.Kumaresan
language: en
Publisher: Leilani Katie Publication
Release Date: 2025-05-03
Dr.V.Kumaresan, Assistant Professor, Department of Computer Science, Arignar Anna Government Arts College, Namakkal, Tamil Nadu, India. Dr. Mukta Makhija, Professor, Assistant Dean - IT, Head - Research and Innovation Cell, Department of Computer Applications, Integrated Academy of Management and Technology((INMANTEC), Ghaziabad, Uttar Pradesh, India. Prof.Kamal Nain, Assistant Professor, Department of Information Technology, Integrated Academy of Management and Technology (INMANTEC), Ghaziabad, Uttar Pradesh, India. Dr.V.A.Jane, Assistant Professor, Department of B.Voc SD and SA, St. Joseph's College (Autonomous), Tiruchirapalli , Tamil Nadu, India. Dr.G.Stephen, Assistant Librarian, St. Xavier's University, Kolkata, West Bengal.
Understanding Machine Learning
Author: Shai Shalev-Shwartz
language: en
Publisher: Cambridge University Press
Release Date: 2014-05-19
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Deep Learning: Algorithms and Applications
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.