Introduction of CNN for Image Recognition- Kernel, Padding, Aggregation, Feature Map, Activation Functions

Convolutional Neural Networks (CNNs) are a class of deep learning models, primarily used for image recognition and processing tasks. Here's a step-by-step introduction to the key concepts:

1. Kernel: In the context of CNN, a kernel (also known as a filter) is a small matrix used to apply effects like blurring, sharpening, edge detection, and more to an image. During the training process, a CNN learns the values of these filters on its own. The kernel is moved across the image to cover all the pixels, performing element-wise multiplication with the part of the image it is currently on, and then summing up the results into a single output pixel.

2. Padding: Padding is a process of adding layers of zeros to the input images. It is used to prevent spatial shrinking of the images when they are passed through the convolutional layers. Padding also helps in improving the performance of the model by allowing it to learn the patterns at the corners and edges of the image.

3. Aggregation: Aggregation in CNN usually refers to pooling layers that follow the convolutional layers. The most common types of pooling are Max Pooling and Average Pooling. These methods reduce the spatial size of the Convolved Feature, controlling overfitting and reducing computational complexity.

4. Feature Map: A feature map is the output of one filter applied to the previous layer. A given filter is designed to detect a specific type of feature in the input. In doing so, the network is able to progressively learn more complex representations of the input data.

5. Activation Functions: Activation functions decide whether a neuron should be activated or not by calculating the weighted sum and further adding bias with it. They are differentiable and help in backpropagation in CNNs. The purpose of the activation function is to introduce non-linearity into the output of a neuron. Common choices for activation functions in CNNs include ReLU (Rectified Linear Unit), sigmoid, and tanh.