Dive into this 15-chapter book on 'Deep Learning Techniques' and how its solutions allow computers to learn from experience and understand hierarchy concepts. It provides approaches to deep learning in areas of detection, prediction, and future framework development. This book presents a concise introduction to recent advances in the field of AI, discussing reinforcement learning and applying deep learning techniques to various domains.
Topics discussed in this book:
- The penetration of social media in society and how women living in India are deprived of mobile services. The study generates research on how gender characterizes individuals
- Discusses agriculture's role and the detection of diseases in plants
- Basic deep learning techniques and how dataset images are compared among model performances
- Talks about image processing fields like surveillance, detection, and recognition. Digital properties have developed dehazing techniques to enhance images and obtain information
- Explains the modification of networking services and how it affects the way of cyberspace communication
- Extensive research on how deep learning is used and challenged by academics, students, and researchers in pointing out accurate metrics and how to increase green AI by reducing its environmental impact
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Pramod Singh Rathore is an assistant professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. He has teaching experience of more than 10 years and has 45 publications in peer-reviewed national and international journals.
Sachin Ahuja, PhD, is a professor in the Department of Computer Science, Chandigarh University, Punjab, India. He has guided several ME and PhD scholars in artificial intelligence, machine learning, and data mining.
Srinivasa Rao Burri is a senior software engineering manager at Western Union, Denver, Colorado. He completed an MS degree in software development from Boston University. He also has received his certifications in Data Science and Machine Learning from Stanford University, Harvard University and Johns Hopkins University. He started his career as a test automation architect in 2004, and has since worked as a leader for many Fortune 500 Organizations advising them on global compliance, data privatization, cloud migration, and AI & ML. He has published multiple articles in international journals.
Ajay Khunteta, PhD, is a dean and professor of computer science and engineering, Poornima University, Jaipur, Rajasthan, India. His research focuses on AI, machine learning, and distributing systems. He has published more than 100 articles in international and national journals and guided 44 M.Tech projects.
Anupam Baliyan, PhD, is Dean of Academic Planning and Research, Galgotias University, India. His research focuses on artificial intelligence, computer networks, computer vision, and machine learning. Along with being a chair and keynote speaker at international conferences, Baliyan has guided more than 20 M.Tech projects and theses.
Abhishek Kumar, PhD, is an associate professor in the Faculty of Engineering, Manipal University, Jaipur, Rajasthan, India and is currently a Post-Doctoral Fellow in Ingenium Research Group Lab, Universidad De Castilla- La Mancha, Ciudad Real, Spain. He has more than 170 publications in peer-reviewed national and international journals and conferences.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : new. Hardcover. This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. Deep learning algorithms and techniques are found to be useful in various areas, such as automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delays in children. Deep Learning Techniques for Automation and Industrial Applications presents a concise introduction to the recent advances in this field of artificial intelligence (AI). The broad-ranging discussion covers the algorithms and applications in AI, reasoning, machine learning, neural networks, reinforcement learning, and their applications in various domains like agriculture, manufacturing, and healthcare. Applying deep learning techniques or algorithms successfully in these areas requires a concerted effort, fostering integrative research between experts from diverse disciplines from data science to visualization. This book provides state-of-the-art approaches to deep learning covering detection and prediction, as well as future framework development, building service systems, and analytical aspects. For all these topics, various approaches to deep learning, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms, are explained. Audience The book will be useful to researchers and industry engineers working in information technology, data analytics network security, and manufacturing. Graduate and upper-level undergraduate students in advanced modeling and simulation courses will find this book very useful. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781394234240
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Buch. Etat : Neu. Neuware - This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. N° de réf. du vendeur 9781394234240
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