Deep Dive into Neural Network Architectures

The Evolution of Neural Networks

Neural networks have evolved from simple perceptrons to complex deep learning architectures that power today's AI applications.

Common Architectures

Feedforward Neural Networks

The simplest type of artificial neural network, where connections between nodes do not form cycles.

Convolutional Neural Networks (CNNs)

Specialized for processing grid-like data such as images. CNNs use convolutional layers to automatically learn spatial hierarchies of features.

Recurrent Neural Networks (RNNs)

Designed for sequential data, RNNs have connections that form directed cycles, allowing them to maintain an internal state or "memory".

Transformers

The architecture behind modern language models, transformers use self-attention mechanisms to process sequential data more efficiently than RNNs.

Choosing the Right Architecture

The choice of architecture depends on your specific task: CNNs for computer vision, RNNs or Transformers for sequential data, and feedforward networks for simpler classification tasks.