Introduction to Deep Learning: Theory and Practice - Mudarakola, Lakshmi Prasad;M, Padmaja
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The Deep Learning textbook provides a comprehensive introduction to the field of deep learning-a branch of machine learning focused on algorithms inspired by the structure and function of the brain's neural networks. It covers foundational topics such as linear algebra, probability, and information theory before delving into core deep learning concepts, including neural networks, optimization techniques, and regularization.Advanced topics include convolutional networks, sequence modeling (such as RNNs), generative models, and deep reinforcement learning. The book also explores theoretical…mehr

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Produktbeschreibung
The Deep Learning textbook provides a comprehensive introduction to the field of deep learning-a branch of machine learning focused on algorithms inspired by the structure and function of the brain's neural networks. It covers foundational topics such as linear algebra, probability, and information theory before delving into core deep learning concepts, including neural networks, optimization techniques, and regularization.Advanced topics include convolutional networks, sequence modeling (such as RNNs), generative models, and deep reinforcement learning. The book also explores theoretical foundations and future directions in deep learning research.Written by leading experts, this textbook is widely used in both academia and industry, offering practical insights alongside deep theoretical knowledge. It's ideal for students, researchers, and professionals looking to understand and apply deep learning techniques.
Autorenporträt
Dr. M. Lakshmi Prasad is a Professor and Head of CSE Division-I at the Institute of Aeronautical Engineering (IARE) in Hyderabad, Telangana. He specializes in Computer Science and Engineering, with a particular focus on Data Science.