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Convolutional Neural Networks in Visual Computing

theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional;

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Convolutional Neural Networks in Visual Computing

theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional;

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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 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 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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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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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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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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Guide to Convolutional Neural Networks

This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance;

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Guide to Convolutional Neural Networks

This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance;

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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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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;

Vergelijkbare producten zoals Artificial Neural Networks and Machine Learning - ICANN 2017

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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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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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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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 Information Processing

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

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Einde inhoud

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