Module Question 7
- What distinguishes “deep learning” from traditional machine learning?
- What are convolutional layers and pooling layers in a CNN, and what is their primary function?
- Why are CNNs particularly well-suited for image processing tasks compared to standard neural networks?
- Name two major applications of CNNs in the real world.
Answer:
- Deep learning uses neural networks with many layers to learn patterns from large amounts of data automatically. Traditional machine learning usually needs humans to choose and prepare the important features first.
- Convolutional layers detect important features in images, like edges, shapes, and textures. Pooling layers reduce the size of the feature maps while keeping the most important information, making the model more efficient.
- CNNs are good at processing images because they can recognize patterns in different parts of an image while using fewer parameters than standard neural networks. This helps them learn visual features more efficiently.
- • Facial recognition: Used to identify or verify people from images.
• Medical imaging: Used to help detect diseases from X-rays, CT scans, and other medical images.