🧠 What is an MLP?
A Multi-Layer Perceptron (MLP) is a fundamental type of feedforward artificial neural network used for supervised learning tasks. It consists of multiple layers of nodes (neurons) and connections, enabling it to learn complex patterns in data.

🧱 Key Components of an MLP

  1. Input Layer

    • Receives raw data features.
    • Example: For image recognition, pixels are input features.
    Neural Network Structure

  2. Hidden Layers

    • Process data through weighted connections and activation functions.
    • Can have one or more layers, depending on task complexity.
    MLP Hidden Layers

  3. Output Layer

    • Produces the final prediction or classification.
    • Size depends on the problem (e.g., 1 neuron for binary classification).

📈 Activation Functions

MLPs use non-linear activation functions to introduce complexity. Common choices include:

  • ReLU (Rectified Linear Unit)
    ReLU Activation Function
  • Sigmoid
    Sigmoid Activation Function
  • Tanh (Hyperbolic Tangent)

🎯 Applications of MLPs

  • Image Classification
  • Natural Language Processing (NLP)
  • Regression Tasks
  • Time Series Forecasting

📘 Further Learning

If you're interested in diving deeper, check out our tutorial on Deep Learning Fundamentals to explore how MLPs fit into the broader neural network landscape.

For hands-on practice, try implementing an MLP using TensorFlow or PyTorch frameworks. 🚀