Convolutional Neural Networks - Yousef's Notes
Convolutional Neural Networks

Convolutional Neural Networks

  • Specialized NN designed for image processing and spatial data.
  • Use: image classification, object detection, facial recognition, medical imaging.
  • Data flow: hierarchical feature extraction, moving from low-level features (edges) to high-level features (objects).
  • Structure: convolutional layers → Pooling layers → Fully connected layers → Output
  • Activation function: ReLU in hidden layers, Sigmoid for binary classification, Softmax for multi-class classification.
  • Loss function: MSE for regression, cross-entropy loss for classification.
  • Learning: uses gradient descent, backpropagation, and filters (called kernels) to extract spatial patterns.
  • Pros: great for images, less parameters than FNNs, translationally invariant.
  • Cons: large labeled datasets, computationally expensive, no temporal awareness.
  • LeNet-5: one of the earliest CNNs that promoted deep learning.
  • Uses convolutional and pooling layers to extract features.
  • Ends with fully connected layers for classification.
  • Inspired modern CNNs like AlexNet, VGG, and ResNet.