Feedforward Neural Networks (FNNs):

• Advantages:
  • Simplicity and ease of implementation.
  • Effective for straightforward classification and regression tasks.
  • Suitable for problems where input and output relationships are static.
• Limitations:
  • Lack of memory; cannot handle sequential data effectively.
  • Limited in capturing spatial hierarchies in data.

Recurrent Neural Networks (RNNs):

• Advantages:
  • Capable of handling sequential and temporal data (e.g., time series, language processing).
  • Maintains memory of previous inputs through internal state (useful in tasks like speech recognition).
• Limitations:
  • Training difficulties due to vanishing and exploding gradient problems.
  • Computationally expensive and slower to train compared to feedforward networks.

Convolutional Neural Networks (CNNs):

• Advantages:
  • Excellent for image and spatial data processing (e.g., object detection, image classification).
  • Efficient feature extraction through convolutional layers, reducing the need for manual feature engineering.
  • Weight sharing reduces the number of parameters, making training faster and models more scalable.
• Limitations:
  • Requires large amounts of labeled data for effective training.
  • Less effective for non-image data or problems where spatial hierarchies are not relevant.

In summary, feedforward neural networks are simple and effective for basic tasks but lack the ability to handle sequences. Recurrent neural networks excel at processing sequential data but face training challenges. Convolutional neural networks are powerful for image-related tasks, leveraging their architecture for efficient feature extraction, but they require substantial data and are less suitable for non-spatial problems.