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Convolutional Neural Networks in Visual Computing

neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;

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Convolutional Neural Networks in Visual Computing

neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;

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Neural Information Processing

follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred;

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Neural Information Processing

; convolutional neural networks; deep neural networks; feature learning and representation; human centred computing; human centred computing and medicine;

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Deep Learning and Convolutional Neural Networks for Medical Image Computing

in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural;

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Spatially Explicit Hyperparameter Optimization for Neural Networks

Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural;

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Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets

in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural;

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Tree-Based Convolutional Neural Networks

This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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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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Neural Network Programming with Java

discussed in this book:-Understanding Neural Networks-Learning in Neural Networks-Java Lego Robots Neural Network-Convolutional Neural Networks;

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Deep Learning from Scratch Building with Python from First Principles

neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural;

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Neural Information Processing

techniques across different domains. The first volume, LNCS 11301, is organized in topical sections on deep neural networks, convolutional neural;

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Biological and Computer Vision

cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among;

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Biological and Computer Vision

cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among;

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Deep Learning in Mining of Visual Content

some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning;

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Applied Neural Networks and Soft Computing

Applied Neural Networks and Soft Computing examines the relation between neural networks and soft computing. Neural network is a system;

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and;

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Proceedings of International Conference on Scientific and Natural Computing

computing, genetic programming, particle swarm optimization, neuro computing, hybrid methods, deep learning, including convolutional neural networks;

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Hands-On Deep Learning with TensorFlow

about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Deep Neural Networks in a Mathematical Framework

gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks;

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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering;

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Introduction to Graph Neural Networks

contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural;

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