Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected nodes (neurons) organized in layers, mimicking how biological neurons process information. This course provides a foundational understanding of neural networks, their architecture, training processes, and applications.
📋 Key Concepts
- Basic Structure: Input layer → Hidden layer(s) → Output layer
- Learning Mechanism: Adjust weights through backpropagation and optimization algorithms
- Activation Functions: Sigmoid, ReLU, Tanh (e.g., 📈 for ReLU visualization)
- Applications: Image recognition, natural language processing, predictive analytics
📚 Extend Your Learning
🔗 Dive deeper into Machine Learning fundamentals to strengthen your foundation before exploring neural networks.