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Image Processing using Pulse-Coupled Neural Networks

. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation;

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Practical Machine Learning and Image Processing

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins;

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Practical Machine Learning and Image Processing

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins;

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Artificial Neural Networks

neural networks, describing: human detection by neural network image processing, camera image formation using a feedforward neural network;

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Neural Networks

Neural networks are used to model complex patterns for prediction and simulation. It uses the processing pattern used by brain neurons to;

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Neural Networks

Neural networks are used to model complex patterns for prediction and simulation. It uses the processing pattern used by brain neurons to;

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Neural Information Processing

approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and;

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Image Processing and Acquisition Using Python

Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing-one;

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Artificial Neural Networks with Java

Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second;

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Artificial Neural Networks and Machine Learning - ICANN 2019: Image Processing

Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented;

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Occam and the Transputer

Examines applications of transputers and OCCAM, including numerical, image processing, neural networks, natural language processing;

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Neural Information Processing

Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully;

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Neural Information Processing

Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully;

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Neural Information Processing

Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully;

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Handbook on Neural Information Processing

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical;

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Deep Learning with Keras

step-by-step functions on a large neural network using the Backpropagation Algorithm * Fine-tune a neural network to improve the quality;

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Learning Approaches in Signal Processing

Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security;

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Image Compression using Neural Networks & Wavelet Transforms

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Advances in Neural Networks - ISNN 2016

This book constitutes the refereed proceedings of the 13th International Symposium on Neural Networks, ISNN 2016, held in St. Petersburg;

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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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Hybrid Machine Intelligence for Medical Image Analysis

-aided detection of mammographic lesions using convolutional neural networks.;

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Practical TensorFlow.js

networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural;

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Practical TensorFlow.js

(CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous;

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous;

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Handbook of Neural Engineering

injuries, and improvements in therapeutic applications using neural prostheses* Neurorobotics and neural rehabilitation engineering--the recent;

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Signal Processing and Image Processing for Acoustical Imaging

, wavelets, and neural networks. It also addresses the important topic of digital signal processing, focusing on the example of homomorphic signal;

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