🧠 Neural Networks: The Building Blocks of Artificial Intelligence

Neural networks are a cornerstone of modern AI, inspired by the human brain's structure and function. They consist of layers of interconnected nodes (neurons) that process data through weighted connections. Here's a quick breakdown:

• Core Concept: Mimic biological neurons to recognize patterns in data
• Key Components:
  • Input layer: Receives raw data
  • Hidden layers: Processes data through computations
  • Output layer: Produces final results
  • Activation functions: Introduce non-linearity (e.g., ReLU, sigmoid)

📌 How They Work

1. Data Flow: Input → Hidden Layers → Output
2. Training Process: Adjust weights via backpropagation and optimization algorithms
3. Types of Networks:
   • Fully Connected Networks (Dense)
   • Convolutional Networks (CNNs)
   • Recurrent Networks (RNNs)

🚀 Applications

• Image recognition: Explore more
• Natural language processing: Learn about NLP
• Time series prediction: Advanced topics

For deeper insights, check our AI fundamentals guide to understand how neural networks fit into the broader AI landscape. 🌐