Deep Cognitive Networks
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Deep Cognitive Networks
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Author : Yan Huang
language : en
Publisher: Springer Nature
Release Date : 2023-03-30
Deep Cognitive Networks written by Yan Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-30 with Computers categories.
Although deep learning models have achieved great progress in vision, speech, language, planning, control, and many other areas, there still exists a large performance gap between deep learning models and the human cognitive system. Many researchers argue that one of the major reasons accounting for the performance gap is that deep learning models and the human cognitive system process visual information in very different ways. To mimic the performance gap, since 2014, there has been a trend to model various cognitive mechanisms from cognitive neuroscience, e.g., attention, memory, reasoning, and decision, based on deep learning models. This book unifies these new kinds of deep learning models and calls them deep cognitive networks, which model various human cognitive mechanisms based on deep learning models. As a result, various cognitive functions are implemented, e.g., selective extraction, knowledge reuse, and problem solving, for more effective information processing. This book first summarizes existing evidence of human cognitive mechanism modeling from cognitive psychology and proposes a general framework of deep cognitive networks that jointly considers multiple cognitive mechanisms. Then, it analyzes related works and focuses primarily but not exclusively, on the taxonomy of four key cognitive mechanisms (i.e., attention, memory, reasoning, and decision) surrounding deep cognitive networks. Finally, this book studies two representative cases of applying deep cognitive networks to the task of image-text matching and discusses important future directions.
Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide
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Author : Willem Meints
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-28
Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide written by Willem Meints and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-28 with Computers categories.
Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key FeaturesUnderstand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to productionEvaluate the performance of your models and improve your deep learning skillsBook Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learnSet up your deep learning environment for the Cognitive Toolkit on Windows and LinuxPre-process and feed your data into neural networksUse neural networks to make effcient predictions and recommendationsTrain and deploy effcient neural networks such as CNN and RNNDetect problems in your neural network using TensorBoardIntegrate Cognitive Toolkit with Azure ML Services for effective deep learningWho this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.
Deep Learning For Robot Perception And Cognition
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Author : Alexandros Iosifidis
language : en
Publisher: Academic Press
Release Date : 2022-02-04
Deep Learning For Robot Perception And Cognition written by Alexandros Iosifidis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Computers categories.
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Cognitive Networks
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Author : Qusay Mahmoud
language : en
Publisher: John Wiley & Sons
Release Date : 2007-09-11
Cognitive Networks written by Qusay Mahmoud and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-11 with Technology & Engineering categories.
Cognitive networks can dynamically adapt their operational parameters in response to user needs or changing environmental conditions. They can learn from these adaptations and exploit knowledge to make future decisions. Cognitive networks are the future, and they are needed simply because they enable users to focus on things other than configuring and managing networks. Without cognitive networks, the pervasive computing vision calls for every consumer to be a network technician. The applications of cognitive networks enable the vision of pervasive computing, seamless mobility, ad-hoc networks, and dynamic spectrum allocation, among others. In detail, the authors describe the main features of cognitive networks clearly indicating that cognitive network design can be applied to any type of network, being fixed or wireless. They explain why cognitive networks promise better protection against security attacks and network intruders and how such networks will benefit the service operator as well as the consumer. Cognitive Networks Explores the state-of-the-art in cognitive networks, compiling a roadmap to future research. Covers the topic of cognitive radio including semantic aspects. Presents hot topics such as biologically-inspired networking, autonomic networking, and adaptive networking. Introduces the applications of machine learning and distributed reasoning to cognitive networks. Addresses cross-layer design and optimization. Discusses security and intrusion detection in cognitive networks. Cognitive Networks is essential reading for advanced students, researchers, as well as practitioners interested in cognitive & wireless networks, pervasive computing, distributed learning, seamless mobility, and self-governed networks. With forewords by Joseph Mitola III as well as Sudhir Dixit.
Developments In Cognitive Radio Networks
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Author : Bodhaswar TJ Maharaj
language : en
Publisher: Springer Nature
Release Date : 2021-07-14
Developments In Cognitive Radio Networks written by Bodhaswar TJ Maharaj and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-14 with Technology & Engineering categories.
This book provides holistic yet concise information on what modern cognitive radio networks are, how they work, and the possible future directions for them. The authors first present the most generic models of modern cognitive radio networks, taking into consideration their different architectural designs and classifications. While the spectrum resource is shown to be the most important resource for the cognitive radio networks, the book exposes the importance of the other resources that are needed to help drive the technology. The book then discusses in-depth the key tools (such as optimization and queuing theory) and techniques (such as cooperative diversity and relaying) that are being employed to formulate resource problems, investigate solutions, and interpret such solutions for useful and practical modern cognitive radio networks realization. Further, the book studies the impact of modern cognitive radio networks on other emerging technologies -- such as 5G, Internet of Things, and advanced wireless sensor networks -- and discusses the role that cognitive radio networks play in the evolution of smart cities and in the realization of a highly interconnected world. In discussing the future of the cognitive radio networks, the book emphasizes the need to advance new or improved tools, techniques, and solutions to address lingering problems in the aspects of resource realization and utilization, network complexity, network security, etc., which can potentially limit the cognitive radio networks in their stride to becoming one of the most promising technologies for the immediate and near future.
Foundations Of Deep Learning
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Author : Tapomoy Adhikari
language : en
Publisher: Tapomoy Adhikari
Release Date : 2023-09-04
Foundations Of Deep Learning written by Tapomoy Adhikari and has been published by Tapomoy Adhikari this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-04 with Education categories.
"Foundations of Deep Learning" offers an erudite exploration into the dynamic landscape of artificial intelligence (AI) and deep learning, authored by Tapomoy Adhikari, an autonomous researcher in the field of Computer Science and Engineering. This scholarly work provides a comprehensive resource suitable for individuals at various stages of expertise, ranging from neophytes to seasoned practitioners within the domain of neural networks. Commencing with an introductory exposition, the book elucidates fundamental principles integral to deep learning. Subsequently, it undertakes a rigorous examination of neural network architectures, elucidating their constituent elements, activation functions, and optimization methodologies. The discourse extends to encompass the intricate mechanisms of backpropagation, a cornerstone process in neural network training. Further chapters delve deeply into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), elucidating their pivotal roles across diverse applications such as computer vision and natural language processing. Noteworthy concepts explored include Generative Adversarial Networks (GANs), Attention Mechanisms, and Transfer Learning, furnishing readers with a comprehensive toolkit to address real-world challenges. In light of burgeoning ethical concerns within the AI landscape, the book offers nuanced insights into ethical considerations pertinent to deep learning. Emphasis is placed on responsible AI model development and its societal implications. The discourse extends to encompass the domain of Natural Language Processing (NLP) integrated with deep learning, elucidating concepts such as word embeddings and sequence-to-sequence models, alongside the transformative potential of attention mechanisms. Deep Reinforcement Learning, a pivotal paradigm underpinning gaming AI and autonomous systems, undergoes meticulous scrutiny, equipping readers with the requisite knowledge to navigate this burgeoning field. As the narrative culminates, readers are prompted to contemplate the future trajectory of deep learning, exploring themes such as neuro-symbolic integration, the potential impact of quantum computing, and the ethical imperatives guiding AI development. "Foundations of Deep Learning" transcends mere instructional discourse, serving as a scholarly compendium elucidating the inner workings of AI architectures shaping contemporary society. Augmented with code snippets, diagrams, and illustrative case studies, this academic endeavor facilitates a practical and accessible understanding of complex concepts. Irrespective of readers' academic or professional affiliations, be it as students, researchers, or engineers, this scholarly treatise equips them with the requisite knowledge and methodologies to navigate the ever-evolving landscape of neural networks.
Computational Models For Cognitive Vision
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Author : Hiranmay Ghosh
language : en
Publisher: John Wiley & Sons
Release Date : 2020-07-01
Computational Models For Cognitive Vision written by Hiranmay Ghosh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.
Learn how to apply cognitive principles to the problems of computer vision Computational Models for Cognitive Vision formulates the computational models for the cognitive principles found in biological vision, and applies those models to computer vision tasks. Such principles include perceptual grouping, attention, visual quality and aesthetics, knowledge-based interpretation and learning, to name a few. The author’s ultimate goal is to provide a framework for creation of a machine vision system with the capability and versatility of the human vision. Written by Dr. Hiranmay Ghosh, the book takes readers through the basic principles and the computational models for cognitive vision, Bayesian reasoning for perception and cognition, and other related topics, before establishing the relationship of cognitive vision with the multi-disciplinary field broadly referred to as “artificial intelligence”. The principles are illustrated with diverse application examples in computer vision, such as computational photography, digital heritage and social robots. The author concludes with suggestions for future research and salient observations about the state of the field of cognitive vision. Other topics covered in the book include: · knowledge representation techniques · evolution of cognitive architectures · deep learning approaches for visual cognition Undergraduate students, graduate students, engineers, and researchers interested in cognitive vision will consider this an indispensable and practical resource in the development and study of computer vision.
Machine Learning And Cognitive Computing For Mobile Communications And Wireless Networks
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Author : Krishna Kant Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2020-07-08
Machine Learning And Cognitive Computing For Mobile Communications And Wireless Networks written by Krishna Kant Singh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-08 with Computers categories.
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
The Cognitive Approach In Cloud Computing And Internet Of Things Technologies For Surveillance Tracking Systems
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Author : Dinesh Peter
language : en
Publisher: Academic Press
Release Date : 2020-03-14
The Cognitive Approach In Cloud Computing And Internet Of Things Technologies For Surveillance Tracking Systems written by Dinesh Peter and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-14 with Science categories.
The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems discusses the recent, rapid development of Internet of things (IoT) and its focus on research in smart cities, especially on surveillance tracking systems in which computing devices are widely distributed and huge amounts of dynamic real-time data are collected and processed. Efficient surveillance tracking systems in the Big Data era require the capability of quickly abstracting useful information from the increasing amounts of data. Real-time information fusion is imperative and part of the challenge to mission critical surveillance tasks for various applications. This book presents all of these concepts, with a goal of creating automated IT systems that are capable of resolving problems without demanding human aid. Examines the current state of surveillance tracking systems, cognitive cloud architecture for resolving critical issues in surveillance tracking systems, and research opportunities in cognitive computing for surveillance tracking systems Discusses topics including cognitive computing architectures and approaches, cognitive computing and neural networks, complex analytics and machine learning, design of a symbiotic agent for recognizing real space in ubiquitous environments, and more Covers supervised regression and classification methods, clustering and dimensionality reduction methods, model development for machine learning applications, intelligent machines and deep learning networks includes coverage of cognitive computing models for scalable environments, privacy and security aspects of surveillance tracking systems, strategies and experiences in cloud architecture and service platform design
Cognitive Computing In Human Cognition
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Author : Pradeep Kumar Mallick
language : en
Publisher: Springer Nature
Release Date : 2020-06-18
Cognitive Computing In Human Cognition written by Pradeep Kumar Mallick and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-18 with Computers categories.
This edited book designs the Cognitive Computing in Human Cognition to analyze to improve the efficiency of decision making by cognitive intelligence. The book is also intended to attract the audience who work in brain computing, deep learning, transportation, and solar cell energy. Due to this in the recent era, smart methods with human touch called as human cognition is adopted by many researchers in the field of information technology with the Cognitive Computing.