Convolutional Neural Networks

Last modified on September 15, 2025 • 1 min read • 98 words
CNN   Computer Vision   Image Processing  
CNN   Computer Vision   Image Processing  
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Understanding CNN architectures for computer vision tasks

Overview   Architecture Components   Convolutional Layers   Pooling Layers   Fully Connected Layers   Popular Architectures   LeNet   AlexNet   VGG   ResNet   DenseNet  

Convolutional Neural Networks (CNNs)  

Overview  

CNNs are specialized neural networks designed for processing grid-like data such as images.

Architecture Components  

Convolutional Layers  

  • Feature extraction through convolution operations
  • Parameter sharing and translation invariance

Pooling Layers  

  • Dimensionality reduction
  • Max pooling vs Average pooling

Fully Connected Layers  

  • Final classification or regression

LeNet  

The pioneering CNN architecture for digit recognition.

AlexNet  

Breakthrough architecture that won ImageNet 2012.

VGG  

Very deep networks with small filters.

ResNet  

Residual connections enabling very deep networks.

DenseNet  

Dense connections for efficient parameter usage.

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