Convolutional Neural Networks (CNNs) In Pytorch

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Convolutional Neural Networks (CNNs) In Pytorch

AI, But Simple Issue #34

Convolutional Neural Networks (CNNs) are the foundational model of modern computer vision applications.

Specifically, they are a special type of neural network designed to process grid-like data, such as images. With their high performance and efficient architecture, they excel in computer vision tasks such as object detection and image classification.

CNNs are particularly effective for image classification tasks due to their ability to capture patterns in images (such as edges, textures, or shapes) through convolutional layers.

In this issue, we’ll use PyTorch to build CNNs, going over model architecture, essential layers, and best practices.

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Before continuing, we recommend having a good understanding of CNNs—especially how they work both conceptually and mathematically. If you don’t already, we have past issues explaining CNNs thoroughly below:

• CNNs, Explained Mathematically (Intermediate)

• Convolutional Neural Networks, Simplified (Beginner)

Additionally, a basic understanding of Python is needed to follow along, specifically some familiarity with the PyTorch framework.

The code in this issue can be executed on a normal home PC and does not require a large amount of VRAM. It can also be executed on hosted notebooks such as Google Colab.