CNN

A Convolutional Neural Network (CNN) is a type of Deep Learning architecture commonly used for image classification and recognition tasks.

It consists of multiple layers, including Convolutional layers, Pooling layers, and fully connected layers. The Convolutional layer applies filters to the input image to extract features, the Pooling layer downsamples the image to reduce computation, and the fully connected layer makes the final prediction.

The network learns the optimal filters through backpropagation and gradient descent.

Advantages of Convolutional Neural Networks (CNNs):

• Good at detecting patterns and features in images, videos and audio signals.
• Robust to translation, rotation and scaling invariance.
• End-to-end training, no need for manual feature extraction.
• Can handle large amounts of data and achieve high accuracy.

Disadvantages of Convolutional Neural Networks (CNNs):

• Computationally expensive to train and require a lot of memory.
• Can be prone to overfitting if not enough data or proper regularization is used.
• Requires large amount of labeled data.
• Interpretability is limited, it’s hard to understand what the network has learned.