How Convolutional Neural Networks (CNN) algorithm works?

 Convolutional Neural Networks (CNNs) are composed of multiple layers that extract increasingly complex and abstract features from the input data. In this section, we will explain how the key components of a CNN work together to enable this feature extraction.

1. Convolutional Layers: The input to a CNN is typically an image represented as a matrix of pixel values. The first layer in a CNN is a convolutional layer that applies a set of filters to the input image to produce a set of feature maps. Each filter learns to detect a specific type of feature, such as an edge or a corner. During the convolution operation, each filter is applied to a small region of the input image, and the result is a scalar value that represents the presence or absence of the feature in that region.

2. ReLU Layers: The output of each convolutional layer is then passed through a Rectified Linear Unit (ReLU) layer, which applies the ReLU activation function to the feature maps. The ReLU function sets all negative values in the feature maps to zero, which introduces non-linearity into the network and allows it to learn more complex features.

3. Pooling Layers: The output of the ReLU layer is then passed through a pooling layer, which reduces the spatial dimensionality of the feature maps by down-sampling them. The most common type of pooling is max pooling, which takes the maximum value in each small region of the feature maps and outputs the result as a single value.

4. Fully Connected Layers: The final layers of the CNN are typically one or more fully connected layers, which use the extracted features to make predictions about the input data. Each neuron in the fully connected layer is connected to all neurons in the previous layer, and the output of the fully connected layer is passed through a softmax activation function to produce a probability distribution over the possible classes.

During training, the CNN learns to adjust the weights of its filters and fully connected layers to minimize the difference between its predicted outputs and the true outputs. This is done using an optimization algorithm such as stochastic gradient descent (SGD), which adjusts the weights based on the gradient of the loss function with respect to the weights.

In summary, CNNs are composed of multiple layers that extract increasingly complex and abstract features from the input data. The convolutional layers learn to detect low-level features such as edges and corners, while the fully connected layers use the extracted features to make predictions about the input data. By adjusting the weights of its layers during training, the CNN learns to recognize patterns in the input data and make accurate predictions.