This course introduces the fundamentals of computer vision and Convolutional Neural Networks (CNNs), which are one of the most important architectures in deep learning for image processing tasks.

The course begins with an introduction to computer vision and explains how machines interpret visual data such as images. It then moves into edge detection, showing how filters can identify important features like borders and shapes within images.

Learners are introduced to convolution operations, including how filters slide across images to extract meaningful patterns. Key concepts such as padding and stride are explained to help control output size and preserve important image information during processing.

The course also covers pooling layers, which reduce the spatial dimensions of feature maps while keeping important information. This helps improve efficiency and reduces overfitting in deep learning models.

A full explanation of a basic convolutional neural network (CNN) is provided, showing how multiple layers work together to detect increasingly complex patterns. From simple edges in early layers to full object recognition in deeper layers, learners gain a clear understanding of how CNNs process images.

By the end of this course, learners will understand how convolutional neural networks function and how they are applied in real-world computer vision tasks such as image classification, object detection, and facial recognition.