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Neural Network Design and the Complexity of Learning

generalize, and summarizes the results, implications, and possible extensions of the work.Neural Network Design and the Complexity of Learning;

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Circuit Complexity and Neural Networks

amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how;

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Deep Learning on Edge Computing Devices

learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and;

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Implementation Techniques

learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based;

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Hardware Architectures for Deep Learning

neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a;

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Neural Network Learning

prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two;

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Neural Network Learning

prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two;

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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with;

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Neural Network Methods in Natural Language Processing

. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics;

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Neural Network Methods in Natural Language Processing

. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics;

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Automated Deep Learning Using Neural Network Intelligence

Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI;

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The Art of Learning

network learning are discussed. The author also discusses neural plasticity and adaptability in smarter neural networks. If we know our human;

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Ultra Wide Band Band Pass Filter

This book provide the Detailed Knowledge of the design of Artificial Neural Network (ANN) based PID controller, to realize fast governor;

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Neural Networks with R

networks and deep learning * Understand the core concepts of artificial neural networks * Understand neurons, perceptrons, bias, weights, and;

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Neural Network Learning in Humans

. It seems that even to a very limited extent, new nerve cells can be built anew in the human central nervous system. Neural network learning;

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Neural Networks with Keras Cookbook

multiple neural network architectures such as CNN, RNN, LSTM in Keras Discover tips and tricks for designing a robust neural network to solve real;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for;

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Codeless Deep Learning with KNIME

comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It'll guide;

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Intrusion Detection with Artificial Neural Networks

of this book is to design, implement and evaluate an anomaly based network intrusion detection system. The System learns about the normal users;

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Navigating Network Complexity

to characterize network complexity, so you can understand it and control it. The authors examine specific techniques and;

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Neural Networks Theory

This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory;

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Learning in Energy-Efficient Neuromorphic Computing - Algorithm and Architecture Co-Design

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning;

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Renewable Energy and Future Power Systems

This book discusses advanced technologies for applications in renewable energy and power systems. The topics covered include neural network;

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Artificial Neural Networks with Java

Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you;

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Artificial Neural Networks with Java

Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you;

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Embedded Deep Learning

; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy - applications, algorithms, hardware;

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Semi-empirical Neural Network Modeling and Digital Twins Development

solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single;

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