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Neural Network Analysis, Architectures and Applications

Neural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter;

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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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Introduction to Neural Networks for Java, Second Edition

. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed.;

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Neural Network Methods in Natural Language Processing

neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures;

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Neural Network Methods in Natural Language Processing

neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures;

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Building Neural Networks

network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author;

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Deep Neural Networks and Applications

Deep Neural Networks and Applications makes the readers aware about the various artificial neural networks (ANN) and the topologies related;

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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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Artificial Neural Network Applications for Software Reliability Prediction

without having analyst capability and expertise in various ANN architectures and their optimization. Artificial neural network (ANN) has;

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Designs and Applied Principles of Artificial Neural Networks

of applications. The aim of this book is to present current developments of architectures, methodologies, and usage of artificial neural networks. The;

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Neural Network Perspectives on Cognition and Adaptive Robotics

using neural network architectures. Covering a wide range of mainstream area and trends, each chapter provides the latest information from a;

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Handbook of Neural Network Signal Processing

, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together;

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Algorithms and Architectures

This volume is the first diverse and comprehensive treatment of algorithms and architectures for the realization of neural network;

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Emerging Deep Learning Applications of Short Range Radars

neural network (DCNN), popular DCNN architectures for computer vision and their features are also introduced. The book presents other deep;

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Neural Network Perspectives on Cognition and Adaptive Robotics

using neural network architectures. Covering a wide range of mainstream area and trends, each chapter provides the latest information from a;

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Advances in Neural Network Research and Applications

aspects of neural network research, including theoretical foundations, architectures, and network organizations, modeling and simulation;

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Mastering PyTorch

neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains;

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Artificial Neural Networks in Pattern Recognition

areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and;

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Neural Networks Modeling and Control

-life neural network systemsIncludes an analysis of potential applications, prototypes and future trends;

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Artificial Neural Networks in Finance and Manufacturing

finance and manufacturing, along with underlying neural network theories and architectures. It offers researchers and practitioners the;

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Deep Neuro-Fuzzy Systems with Python

provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training;

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Adaptive Sliding Mode Neural Network Control for Nonlinear Systems

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control;

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Mastering Computer Vision with TensorFlow 2.x

analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network;

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault;

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Advanced Computing

; Using Natural Language Processing for Solving Text and Language related Applications; Using Different Neural Network Architectures for;

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Advanced Computing

; Using Natural Language Processing for Solving Text and Language related Applications; Using Different Neural Network Architectures for;

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Introduction to the Theory of Neural Computation

coverage of neural network applications in a variety of problems of both theoretical and practical interest.;

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