Lalitha Krishnasamy (35 results)

Condition: As New. Unread book in perfect condition.

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.

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Paperback. Condition: Brand New. 298 pages. 10.00x7.00x10.00 inches. In Stock.

Hardcover. Condition: Very Good. No Jacket. Missing dust jacket; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condition: new. Paperback. Neuromorphic Computing for Brain Computer Interfaces: Enhanced Synergies in Mind and Machine Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condition: New. Provides an in-depth study of the principles, technology, and applications that drive neuromorphic computing and brain-computer interfacesOffers an interdisciplinary approach that includes cognitive science, neuroscience, artificial intelli.

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.…

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Condition: New. Dr. Lalitha Krishnasamy received B.Tech IT from CSI College of Engineering, The Nilgris in 2005 and M.Tech IT from Anna University of Technology in 2009 and completed PhD in Wireless Sensor Networks in 2019. Currently working as an Assoc.

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Taschenbuch. Condition: Neu. Neuware - Neuromorphic Computing for Brain Computer Interfaces: Enhanced Synergies in Mind and Machine examines practical situations where interpretability is crucial, going beyond theoretical considerations and providing case studies and examples to illustrate how Brain Computer Interfaces could be implemented in the real world. The book encompasses novel concepts, cutting-edge research, frameworks, and tools that facilitate comprehension of neuromorphic computing models. It is an ideal, comprehensive reference for professionals, researchers, and students who want to grasp the fundamental concepts and most recent developments in brain-computer interfaces (BCIs) and neuromorphic computing.…

Paperback. Condition: new. Paperback. Neuromorphic Computing for Brain Computer Interfaces: Enhanced Synergies in Mind and Machine Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condition: new. Questo è un articolo print on demand.

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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. 424 pp. Englisch.…