Neural networks are a cornerstone of modern artificial intelligence, inspired by the structure and function of the human brain. They consist of interconnected nodes (neurons) organized in layers, enabling machines to recognize patterns, make decisions, and solve complex problems.

Key Concepts 📚

  • Layers: Input, hidden, and output layers. The input layer receives data, hidden layers process it, and the output layer provides predictions.
  • Neurons: Mathematical operations that process inputs and produce outputs using weights and biases.
  • Activation Functions: Non-linear functions like ReLU (Rectified Linear Unit) or Sigmoid that determine neuron output.
    📌 Example: ReLU is widely used for its simplicity and effectiveness in deep learning.

How They Work 🔍

  1. Data Input: Features are fed into the input layer.
    Neural_Network_Structure
  2. Weighted Sum: Neurons calculate the sum of inputs multiplied by weights.
    Activation_Function_ReLU
  3. Output Generation: Through multiple layers, the network refines predictions until the output layer delivers the final result.

Applications 🌍

Next Steps 🚀

  • Dive deeper into machine learning fundamentals to build a stronger foundation.
  • Experiment with simple neural network implementations using frameworks like TensorFlow or PyTorch.

Let me know if you'd like to visualize a sigmoid function or explore neural_network_architecture further! 📌