An Introduction to Convolutional Neural Networks
An Introduction to Convolutional Neural Networks
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DOI：
10.22214/ijraset.2022.47789
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发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
Aarush Saxena
中科院分区：
文献类型：
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作者：
Aarush Saxena
翻译后摘要：机器学习领域已经采取了戏剧性的转折，在最近的时代，与人工神经网络（ANN）的兴起。这些受生物启发的计算模型可以远远超过以前形式的人工智能在普通机器学习任务中的性能。ANN架构中最令人印象深刻的形式之一是卷积神经网络（CNN）。CNN主要用于解决困难的图像驱动模式识别任务，并以其精确而简单的架构提供了一种简化的ANN入门方法。本文简要介绍了CNN，讨论了最近发表的论文和开发该法案的新技术--非常奇妙的图像识别模型。本介绍假设您熟悉ANN和机器学习基础知识。
Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous forms of artificial intelligence in common machine-learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve difficult image-driven pattern recognition tasks and with their precise yet simple architecture, offer a simplified method of getting started with ANNs. This document briefly introduces CNNs, discussing recently published papers and newly formed techniques in developing this bill- leniently fantastic image recognition models. This introduction assumes you are familiar with ANNs and machine learning fundamentals.