卷积神经网络

M. Véstias
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引用次数: 4

摘要

机器学习是研究计算系统在模式识别和推理的基础上完成任务的算法和模型。当开发一种算法来完成特定任务是困难的或不可行的时候,机器学习算法可以根据以前的训练数据提供输出。一个著名的机器学习模型是深度学习。最新的深度学习模型是基于人工神经网络(ANN)的。人工神经网络有几种类型,包括前馈神经网络、Kohonen自组织神经网络、循环神经网络、卷积神经网络、模块化神经网络等。本文重点介绍了卷积神经网络的模型、训练和推理过程及其适用性。它还将概述最常用的CNN模型,以及对下一代CNN模型的期望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Network
Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.
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