Ethical AI – Jupiter Publications Consortium https://jpc.in.net Best Publishing House in India Wed, 07 Feb 2024 12:23:55 +0000 en-US hourly 1 https://wordpress.org/?v=6.7.1 https://i0.wp.com/jpc.in.net/wp-content/uploads/2023/07/logo-Copy.png?fit=32%2C32&ssl=1 Ethical AI – Jupiter Publications Consortium https://jpc.in.net 32 32 221206694 Deep Think: The Revolutionary Depths of Machine Learning https://jpc.in.net/product/deep-learning-and-beyond-ais-new-horizons/ https://jpc.in.net/product/deep-learning-and-beyond-ais-new-horizons/#respond Sun, 24 Dec 2023 16:19:16 +0000 https://jpc.in.net/?post_type=product&p=25292 www.doi.org/10.47715/JPC.978-93-91303-89-1 Price: 350/- No. of. Pages: 178 Jupiter Publications Consortium 22/102, Second Street Venkatesa Nagar, Virugambakkam Chennai 600 092, Tamil Nadu, India Website: www.jpc.in.net Printed by: Magestic Technology Solutions (P) Ltd]]> Abstract

This monograph provides a comprehensive exploration of deep learning, a transformative technology in the field of artificial intelligence. Beginning with an introduction to the background of machine learning and the rise of deep learning, the text delves into the historical evolution of artificial intelligence and neural networks. It offers a detailed examination of the foundations of deep learning, including neural network basics, architecture, activation functions, and essential techniques like backpropagation and gradient descent. The work then progresses to advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms, including transformers. A significant focus is placed on practical aspects, like training deep models, regularization techniques, optimization strategies, and the utilization of transfer learning with pre-trained models. The monograph also explores the diverse applications of deep learning in fields such as image recognition, natural language processing, and autonomous systems, while addressing the challenges of overfitting, interpretability, and ethical considerations. Looking forward, it examines emerging trends like quantum neural networks, neural architecture search, and the intersection of neuroscience and deep learning. The text concludes with insightful case studies, reflecting on real-world implementations and milestones in deep learning. This work serves as a valuable resource for understanding the current state of deep learning, its practical applications, and future directions.

Keywords: Deep Learning, Machine Learning, Artificial Intelligence, Neural Networks, Backpropagation, Gradient Descent, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Training Models, Regularization, Optimization, Transfer Learning, Image Recognition, Natural Language Processing, Autonomous Systems, Overfitting, Interpretability, Ethical Considerations, Quantum Neural Networks, Neural Architecture Search, Neuroscience.

]]> https://jpc.in.net/product/deep-learning-and-beyond-ais-new-horizons/feed/ 0 25292 TRANSFER LEARNING AND DOMAIN ADAPTATION IN DEEP NETWORKS https://jpc.in.net/product/transfer-learning-and-domain-adaptation-in-deep-networks/ https://jpc.in.net/product/transfer-learning-and-domain-adaptation-in-deep-networks/#respond Sun, 05 Nov 2023 10:59:27 +0000 https://jpc.in.net/?post_type=product&p=25256 ISBN: 978-93-91303-90-7 First Published: August 2023 DOI: www.doi.org/10.47715/JPC.B.978-93-91303-90-7 Price: 400/- No. of. Pages: 150 Jupiter Publications Consortium 22/102, Second Street Venkatesa Nagar, Virugambakkam Chennai 600 092, Tamil Nadu, India Website: www.jpc.in.net Printed by: Magestic Technology Solutions (P) Ltd   How to Cite Your Book: Shamreen Ahamed, B., Dharani, V., Poonkodi, M., Sangeetha, G., & Sanaa Fathima, B. (2023). Transfer Learning and Domain Adaptation in Deep Networks. Chennai, IN: Jupiter Publications Consortium. www.doi.org/10.47715/JPC.B.978-93-91303-90-7]]> Abstract
The rapid evolution of deep learning has opened a plethora of opportunities across various fields, but not without its share of challenges. One of the most significant hurdles is the requirement for extensive labeled datasets to train robust models, which are often expensive, time-consuming, or infeasible to obtain. “Transfer Learning and Domain Adaptation in Deep Networks” addresses this challenge by providing an in-depth exploration of transfer learning and domain adaptation techniques, which allow for the transfer of knowledge from one domain to solve problems in another, thereby mitigating the data scarcity problem. This book presents a structured analysis of the foundational theories, cutting-edge architectures, practical applications, and the future prospects of transfer learning and domain adaptation. It scrutinizes the benefits and intricacies of these approaches, discusses the ethical implications of biased models, and offers insights into the creation of fair and unbiased AI systems. With a focus on current trends and future directions, this comprehensive text serves as a critical resource for those looking to deepen their understanding of these transformative techniques in deep learning.
Keywords: Transfer Learning, Domain Adaptation, Deep Learning, Artificial Intelligence, Ethical AI

 

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