Introduction
Neural networks are a cornerstone of modern machine learning, inspired by the human brain's structure and function. They excel at pattern recognition and complex data modeling. For a deeper dive, check out our Neural Networks Fundamentals course.
Key Concepts
- Perceptron: The basic building block of neural networks, capable of learning linear classification tasks.
- Backpropagation: A critical algorithm for training networks by adjusting weights through gradient descent.
- Activation Functions: Non-linear functions like ReLU or Sigmoid that introduce complexity into models.
Applications
- 🖼️ Image Recognition: Networks like CNNs are used in facial identification and object detection.
- 📝 Natural Language Processing: Transformers and RNNs enable chatbots and language translation.
- 📈 Predictive Analytics: Applied in stock market forecasting and medical diagnosis.