Convolutional Neural Networks (CNNs) are a type of deep learning model used for image recognition and processing. They mimic the human brain’s visual perception, identifying patterns, edges, and features in images. CNNs power applications like facial recognition, object detection, and medical imaging, making them essential for AI-driven vision tasks.

A Vanilla RNN is a basic Recurrent Neural Network used for processing sequential data, like text and time series. It has a feedback loop that allows information to persist, making it useful for speech recognition, language modeling, and time-series prediction. However, it struggles with long-term dependencies due to vanishing gradients. 

LSTM (Long Short-Term Memory) is an advanced type of Recurrent Neural Network (RNN) designed to handle long-term dependencies in sequential data. It uses gates (input, forget, and output) to control information flow, making it effective for speech recognition, language translation, and time-series prediction while overcoming the vanishing gradient problem.