🧠 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, mimicking biological neural pathways.
Key Concepts
- Layers:
- Input layer: Receives data (e.g., images, text).
- Hidden layer: Processes data through weighted connections.
- Output layer: Produces predictions or classifications.
- Activation Functions: Introduce non-linearity (e.g., ReLU, sigmoid).
- Training: Adjusts weights via backpropagation and optimization algorithms.
Types of Neural Networks
🔄 Feedforward Neural Networks (FNN): Simplest form, data flows in one direction.
📊 Convolutional Neural Networks (CNN): Specialized for image processing.
🤖 Recurrent Neural Networks (RNN): Handle sequential data like time series or text.
Applications
- Image recognition
- Natural language processing
- Autonomous systems
- Predictive analytics
For a deeper dive into AI fundamentals, explore our AI Overview guide.