What is a Neural Network?
A neural network is a computational model inspired by the human brain. It consists of interconnected nodes (neurons) organized in layers, mimicking the way biological neurons process information.
Key Components
- Layers: Input, hidden, and output layers
- Weights & Biases: Adjustable parameters for learning
- Activation Functions: Non-linear transformations (e.g., ReLU, Sigmoid)
- Loss Function: Measures prediction error
- Optimization Algorithm: Adjusts weights (e.g., Gradient Descent)
Learning Process
- Forward Propagation: Input data flows through layers to produce an output
- Backpropagation: Error is calculated and propagated backward to adjust weights
- Iteration: Repeat until model performance improves
Applications
- Image recognition 🖼️
- Natural language processing 💬
- Time series forecasting 📈
- Reinforcement learning 🏆
Further Reading
For deeper insights, explore our Machine Learning Fundamentals tutorial.