Convolutional Neural Network

1. What’s Convolutional Neural Network?

A Convolutional Neural Network (CNN) is a type of deep neural network architecture designed specifically for the efficient processing of grid-structured data, such as images and videos. CNNs are highly effective in computer vision tasks, visual pattern recognition, and image classification.

2. What’s the Convolutional Neural Network functions?

The key feature of Convolutional Neural Networks is the convolution layer, which uses filters to detect local patterns in small regions of the input. These filters are applied repeatedly across the image, allowing the network to capture hierarchical and complex features at different levels of abstraction. In addition to convolution layers, CNNs also typically include pooling layers to reduce dimensionality and fully connected layers to perform final classification.

CNNs have different use cases due to their application in the visual, for example, a CNN can be implemented in an e-commerce platform so that the user uploads images of products, the algorithm can detect the similar clothing that the user wants. and thus the platform could recommend clothing that was similar to the one uploaded by the user. A no less important fact is that Tesla makes use of convolutional neural networks for the visual segmentation of the elements of public roads, traffic lights, people, other vehicles, etc., this segmentation allows the automation of cars in Tesla, making this type of Neural networks bring automation to the transportation sector.

3. Examples of Convolutional Neural Network.

• LeNet.
• AlexNet.
• ResNet.
• VGG.
• Inception.
• ZFNet
• PolyNet.