Authors: Mrs. V. Selvarani, Mr. A. Kottaichamy, Dr. M. Gokiladevi and Mrs. J. Uma Maheswari

ISBN: 978-81-993282-4-2

DOI: https://doi.org/10.59646/dl/457

Date of Publication: September 17, 2025

About the Book:

Deep Learning for Data Science: Exploring Neural Networks and AI Models offers a structured and in-depth journey into the rapidly evolving world of artificial intelligence, with a special emphasis on the role of neural networks in modern data science. Beginning with the fundamentals, the book introduces readers to the historical milestones of neural network research, the architecture and functioning of neurons, layers, and activation functions, and the distinction between deep learning and traditional machine learning approaches. Building upon these foundations, it delves into the essential mathematics underpinning deep learning—including linear algebra, calculus, probability, and optimization methods—equipping readers with the tools needed to understand model training, backpropagation, and regularization techniques.

The book then transitions into practical applications of supervised learning, covering classification and regression tasks, evaluation metrics, and hyperparameter tuning, before exploring unsupervised learning techniques such as clustering, dimensionality reduction, and autoencoders for feature learning and anomaly detection. Special emphasis is placed on advanced architectures such as Convolutional Neural Networks (CNNs) for image processing, Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) for sequence modeling, and Generative Adversarial Networks (GANs) for creative AI applications like image generation and data augmentation. Through carefully explained workflows and real-world examples, readers learn how to design, implement, and optimize these models using industry-standard frameworks like TensorFlow, Keras, and PyTorch.

In its advanced sections, the book highlights cutting-edge topics shaping the future of AI, including reinforcement learning, neural architecture search (NAS), explainable AI (XAI), and multi-modal learning. Ethical considerations, responsible AI practices, and emerging applications in domains such as healthcare, autonomous systems, and quantum computing are discussed to broaden the reader’s perspective. With its blend of theoretical depth, mathematical rigor, hands-on exercises, and future-oriented insights, this book serves as both a foundational text for students and a practical guide for professionals and researchers seeking to harness the transformative power of deep learning in data science.