Question15Max. score: 3.00Explain the difference between CNN and RNN architectures. It's usage?  Give one example of each

Sure! I will explain the difference between CNN and RNN architectures and their usage, and provide one example of each.

1. CNN (Convolutional Neural Network):

• CNN is primarily used for image and video processing tasks.
• It consists of convolutional layers that extract features from the input data.
• CNNs are designed to automatically learn spatial hierarchies of features from images.
• They are effective in tasks such as image classification, object detection, and image segmentation.
• Example: The VGG16 architecture, which is a popular CNN model used for image classification.

2. RNN (Recurrent Neural Network):

• RNN is mainly used for sequential data processing tasks.
• It has recurrent connections that allow information to persist over time.
• RNNs are suitable for tasks that involve sequential dependencies, such as natural language processing and speech recognition.
• They can handle variable-length input sequences.
• Example: Long Short-Term Memory (LSTM) networks, which are a type of RNN commonly used for language modeling and speech recognition.

In summary, CNNs are best suited for image and video processing tasks, while RNNs are more suitable for sequential data processing tasks.