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  1. Convolutional neural network - Wikipedia

    Convolutional neural networks are variants of multilayer perceptrons, designed to emulate the behavior of a visual cortex. These models mitigate the challenges posed by the MLP …

  2. CNN 入门讲解:什么是卷积(Convolution)? - 知乎

    CNN 又叫 Convolutional neural network, 中文名有叫卷积神经网络,它怎么来的,它有多牛逼,这就不多说了,大家网上查。 希望大家在看之前有一点点基本的 computer vision 和CNN 的基 …

  3. 卷积神经网络_百度百科

    卷积神经网络(Convolutional Neural Network, CNN)是一种前馈神经网络(Feedforward Neural Networks),广泛应用于图像识别和视觉任务,是深度学习中的核心模型之一。

  4. 卷積神經網路 Convolutional Neural Networks · 資料科學・機器 …

    每當深度學習又有什麼重大突破時,這些進展十有八九都和 卷積神經網路 (Convolutional Neural Networks,CNN)有關。 CNN 又被稱為 CNNs 或 ConvNets,它是目前 深度神經網路 …

  5. Convolutional Neural Networks - IEEE Xplore

    This chapter presents convolutional neural networks (CNNs) that are often classifiers, so a CNN can be classifying neural network. A CNN is an ANN that includes at least one convolutional …

  6. Introduction to Convolution Neural Network - GeeksforGeeks

    Jul 11, 2025 · Convolutional Neural Network (CNN) is an advanced version of artificial neural networks (ANNs), primarily designed to extract features from grid-like matrix datasets. This is …

  7. Convolution - Wikipedia

    Convolutional neural networks represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, time series analysis …

  8. 【综述】一文读懂卷积神经网络 (CNN) - 知乎

    卷积神经网络(Convolutional Neural Networks, CNN)是一类包含卷积计算且具有深度结构的前馈神经网络(Feedforward Neural Networks),是深度学习(deep learning)的代表算法之一。

  9. Convolutional Neural Network: A Complete Guide - LearnOpenCV

    Jan 18, 2023 · Convolutional Neural Network (CNN) forms the basis of computer vision and image processing. In this post, we will learn about Convolutional Neural Networks in the context of an …

  10. What are Convolutional Neural Networks? | IBM

    The convolutional layer is the core building block of a CNN, and it is where the majority of computation occurs. It requires a few components, which are input data, a filter and a feature …