All three – RNN, CNN, and ANN – are types of artificial neural networks used in deep learning, but each excels at different tasks due to their unique architectures. Here’s a breakdown:

• Artificial Neural Network (ANN):

  • The foundation for the others, resembling a simplified brain with interconnected layers of nodes that process information.
  • Information flows forward through the network, making them good for general-purpose tasks like classification or prediction.
  • Think of them as learning general patterns from data.

• Convolutional Neural Network (CNN):

  • Specialized for visual data like images and video.
  • Uses filters to identify patterns within the data, like edges or shapes, making them highly effective in image recognition and computer vision tasks.
  • Imagine them like looking at smaller sections of an image to understand the whole picture.

• Recurrent Neural Network (RNN):

  • Designed to handle sequential data like text or speech.
  • They can process information in a sequence, with a “memory” of past inputs that influences their understanding of current inputs.
  • This makes them well-suited for tasks like language translation, machine translation, and sentiment analysis.
  • Think of them like reading a sentence where each word informs your understanding of the next.