Neural Network Architecture: A Beginner's Guide

🧠 Understanding Neural Network Architecture is fundamental to building effective machine learning models. This tutorial explores key concepts and components of neural networks, from basic layers to advanced architectures.

Key Components of Neural Networks

  1. Layers

    • Input Layer: Receives raw data (e.g., images, text).
    • Hidden Layers: Process data through transformations (e.g., dense_layer, convolutional_layer).
    • Output Layer: Produces final predictions (e.g., softmax_layer, sigmoid_layer).

  2. Activation Functions

    • Non-linear functions like ReLU, Sigmoid, and Tanh enable networks to learn complex patterns.

  3. Neurons & Connections

    • Nodes (neurons) connected via weights and biases form the backbone of neural networks.

Common Architectures

  • Feedforward Neural Networks (FNN): Basic sequential structure for tasks like classification.
  • Convolutional Neural Networks (CNN): Specialized for image processing (e.g., CNN in /tutorial/computer_vision).
  • Recurrent Neural Networks (RNN): Designed for sequential data (e.g., time series, NLP).

Applications

  • Image recognition
  • Natural language processing
  • Time series forecasting
Neural Network Architecture

For deeper insights, explore our guide on deep learning fundamentals. 📚