The video tutorial introduces convolution operations, highlighting their efficiency over dense neural networks for image processing. It explains the concept of kernels, their role in feature engineering, and how they are used in convolution operations. The tutorial also covers stride and padding, which are crucial for maintaining image dimensions during convolution. Additionally, it discusses pooling operations, including mean and max pooling, which help in reducing the spatial dimensions of images. The video concludes with a brief overview of the next steps in convolution operations.

What is padding and why is it used in convolution operations?

Describe the difference between mean pooling and max pooling.