🧠 What are Neural Networks?
Neural networks are computational models inspired by the human brain. They consist of layers of interconnected nodes (neurons) that process data to recognize patterns, make decisions, or predict outcomes.
Key Concepts
- Neurons: Basic units that receive input, process it, and produce output.
- Layers:
- Input Layer: Receives raw data.
- Hidden Layer(s): Processes data through weighted connections.
- Output Layer: Produces the final result.
- Activation Functions: Introduce non-linearity (e.g., ReLU, Sigmoid).
Types of Neural Networks
- Feedforward Neural Networks (FNN): Simplest structure, data flows in one direction.
- Convolutional Networks (CNN): Specialized for image processing.
- Recurrent Networks (RNN): Handle sequential data (e.g., text, time series).
Applications
- Image recognition 🖼️
- Natural language processing 💬
- Autonomous vehicles 🚗
- Financial forecasting 💰