Neural networks are a cornerstone of modern machine learning, inspired by the structure and function of the human brain. They consist of layers of interconnected nodes (neurons) that process information through weighted connections. Here's a breakdown:
📌 Key Concepts
- Artificial Neurons: Mimic biological neurons, taking inputs, applying weights, and passing through an activation function (e.g., ReLU, sigmoid).
- Layers: Typically include an input layer, hidden layers, and an output layer. The hidden layers perform complex feature extraction.
- Training: Adjusts weights using algorithms like gradient descent to minimize errors in predictions.
🧪 Structure Visualization
🌐 Applications
- Image recognition (e.g., CNNs)
- Natural language processing (e.g., RNNs)
- Time series forecasting (e.g., LSTMs)
- Game playing (e.g., AlphaGo)
📚 Further Reading
For a deeper dive into deep learning fundamentals, check out our guide on Deep Learning Basics. 🚀