Welcome to the tutorial on model architecture! Understanding the structure of neural networks is crucial for building effective machine learning models. Below are key concepts and examples to guide you.

Common Model Architectures 🏗️

  • Convolutional Neural Networks (CNN)
    Ideal for image processing tasks. Use [CNN] for visualizing filters and layers.

    Convolutional Neural Network

  • Recurrent Neural Networks (RNN)
    Designed for sequential data like text or time series.

    Recurrent Neural Network

  • Transformer Models
    Revolutionized natural language processing with self-attention mechanisms.

    Transformer

Key Components 🛠️

  • Input layer: Accepts raw data
  • Hidden layers: Process data through weights and activation functions
  • Output layer: Produces final predictions
  • Activation functions: Introduce non-linearity (e.g., ReLU, Sigmoid)

For deeper insights, explore our guide on deep learning fundamentals. Happy learning! 🚀