Introduction to LeNet-5

Introduction to LeNet-5

LeNet-5 is a convolutional neural network (CNN) architecture that introduced several key features and innovations that have become standard in modern deep learning. It demonstrated the effectiveness of CNNs for image recognition tasks and introduced key concepts such as convolution, pooling, and hierarchical feature extraction that underpin modern deep learning models....

Architecture of LeNet-5

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Detailed Explanation of the Layers

Convolutional Layers (Cx): These layers apply convolution operations to the input, using multiple filters to extract different features. The filters slide over the input image, computing the dot product between the filter weights and the input pixels. This process captures spatial hierarchies of features, such as edges and textures.Subsampling Layers (Sx): These layers perform pooling operations (average pooling in the case of LeNet-5) to reduce the spatial dimensions of the feature maps. This helps to control overfitting, reduce the computational load, and make the representation more compact.Fully Connected Layers (Fx): These layers are densely connected, meaning each neuron in these layers is connected to every neuron in the previous layer. This allows the network to combine features learned in previous layers to make final predictions....