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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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Neural Network Methods in Natural Language Processing

, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These;

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Neural Network Methods in Natural Language Processing

, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These;

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Neural Network Programming with Java

value will be calculated based on the value obtained in the previous step. You will learn how to implement such a network in Java. Convolutional;

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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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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 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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Neural Information Processing

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

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Practical Deep Learning

networks, and modern convolutional neural networks. Experiments in Python - working with leading open-source toolkits and standard datasets - give;

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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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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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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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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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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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Introduction to Graph Neural Networks

between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural;

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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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Hands-on Machine Learning with Python

third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks;

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Advanced Deep Learning with R

speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using;

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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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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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