Neural Network Architectures

In this section, we explore how neural networks are designed and how they truly work, examining both the mathematical principles and the intuitive ideas that drive some of the most widely used neural network architectures.

Topics

• Convolutional Neural Networks
  Explore how CNNs use convolutional layers to automatically extract features from images, enabling powerful solutions for tasks like image classification and object detection.

• Residual Networks
  Discover how ResNets use skip connections to enable the training of very deep networks, addressing the vanishing gradient problem and improving performance in complex tasks.

• Recurrent Neural Networks
  Learn how RNNs process sequential data by maintaining memory of previous inputs.