Neural networks are a fundamental concept in machine learning, inspired by the human brain's ability to process information. They consist of layers of interconnected nodes (neurons) that learn patterns from data through training.
Key Components 🧩
- Input Layer: Receives data features
- Hidden Layers: Process data through weighted connections
- Output Layer: Produces predictions or classifications
- Activation Functions: Introduce non-linearity (e.g., ReLU, Sigmoid)
Types of Neural Networks 🌐
- Feedforward Neural Networks (FNN): Standard architecture for basic tasks
- Convolutional Neural Networks (CNN): Specialized for image processing
- Recurrent Neural Networks (RNN): Handles sequential data like time series
- Autoencoders: Used for dimensionality reduction
Applications 🚀
- Image recognition 📸
- Natural language processing 🗣️
- Predictive analytics 📈
- Generative models 🎨
For deeper exploration, check our tutorial on building neural networks. Want to dive into specific architectures? Explore CNN详解 or RNN原理.