Deep Learning Models Foundations And Applications
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Deep Learning Models: Foundations and Applications
Author: Mr.Balingan Sangameshwar
language: en
Publisher: Leilani Katie Publication
Release Date: 2025-05-01
Mr.Balingan Sangameshwar, Assistant Professor, Department of Computer Science and Engineering - (CyS, DS) and AI & DS, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India. Mr.Vonteru Srikanth Reddy, Assistant Professor, Department of Computer Science and Engineering (Data Science), Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India. Mr.P.Praveen, Assistant Professor, Department of Computer Science and Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India. Mr.Sudheer Nidamanuri, Assistant Professor, Department of Computer Science and Engineering - (CyS, DS) and AI & DS, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, India
Data Science: Foundations and Applications
The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025. The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.
Machine Learning Foundations and Applications: A Practical Guide to Supervised, Unsupervised, and Reinforcement Learning
Master the algorithms powering today’s AI revolution. This practical guide breaks down the foundations of machine learning into clear, structured lessons—covering supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, developer, or data professional, you'll learn how real-world models like linear regression, neural networks, support vector machines, PCA, and Q-learning actually work—mathematically and computationally. This book blends theory with implementation, offering step-by-step explanations, intuitive insights, and practical tools for applying machine learning in business, research, and product development. If you're serious about learning machine learning, this is the book that takes you from first principles to advanced concepts—with clarity, depth, and purpose.