🧠 What is a Neural Network?
A neural network is a computational model inspired by the human brain, designed to recognize patterns and make decisions. Think of it as a web of interconnected nodes (neurons) that process information through layers.

🧱 Key Components

  1. Layers

    • Input layer: Receives raw data (e.g., images, text).
    • Hidden layers: Process data through weighted connections.
    • Output layer: Produces the final result (e.g., predictions).
    Neural Network Structure

  2. Neurons & Activation Functions
    Each neuron applies an activation function (e.g., ReLU, Sigmoid) to transform inputs.

    Perceptron

  3. Weights & Biases
    Adjust these parameters during training to minimize errors.

🔄 How Neural Networks Learn

  1. Forward Propagation
    Data flows through layers, and predictions are made.
  2. Loss Function
    Measures the difference between predicted and actual outputs.
    Loss Function
  3. Backpropagation
    Adjusts weights using gradient descent to reduce loss.
  4. Optimization
    Algorithms like Adam or SGD refine the model's performance.

📈 Applications of Neural Networks

  • Image Recognition: Explore more
  • Natural Language Processing (NLP):
    NLP
  • Autonomous Vehicles: Combines sensors and neural networks for real-time decision-making.

Tip: For hands-on practice, try building a simple network using frameworks like TensorFlow or PyTorch. 🧪
Extended Reading: Dive deeper into Neural Networks Fundamentals for foundational concepts.