CNN process

CNN (Convolutional Neural Network) is a type of neural network commonly used in image recognition and computer vision tasks. The general process of CNN can be broken down into the following steps:

1. Convolution: In this step, a filter or kernel is applied to the input image to produce a feature map. The filter slides over the image, performing a dot product between the filter weights and the pixel values in the image.

2. ReLU: The ReLU (Rectified Linear Unit) activation function is then applied to the output of the convolution step. This step helps to introduce non-linearity into the network and make it more powerful.

3. Pooling: In this step, the size of the feature map is reduced by taking the maximum or average value of each subregion of the feature map. This helps to make the network more robust to variations in the input image.

4. Fully Connected Layers: The output of the pooling layer is then passed through one or more fully connected layers. These layers perform a matrix multiplication between the input and a weight matrix, followed by an activation function like ReLU or softmax.

5. Output: The final output of the network is a probability distribution over the possible classes in the input image.

The above process is repeated for each image in a dataset during the training phase. The weights of the filters and fully connected layers are adjusted using backpropagation and gradient descent to minimize the loss function and improve the accuracy of the network.