🧠 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 through weighted connections, enabling them to learn patterns from large datasets.
Key Components
- Input Layer: Receives raw data (e.g., images, text).
- Hidden Layer(s): Processes data through nonlinear transformations.
- Output Layer: Produces the final result (e.g., classification, prediction).
How They Work
- Forward Propagation: Data flows through the network, with each layer applying weights and activation functions.
- Backward Propagation: Adjusts weights using gradient descent to minimize errors.
- Activation Functions: Introduce nonlinearity (e.g., ReLU, Sigmoid).
Applications
- 🖼️ Image Recognition: Detecting objects in photos (e.g.,
image_recognition). - 📖 Natural Language Processing: Understanding text (e.g.,
natural_language_processing). - 💰 Financial Prediction: Forecasting market trends (e.g.,
financial_prediction).
For a deeper dive into machine learning concepts, check our Machine Learning Fundamentals Tutorial.