Neural networks are a cornerstone of modern artificial intelligence (AI) and machine learning. Inspired by the human brain, they consist of interconnected nodes (neurons) organized in layers. Here's a concise overview:
🧠 Basic Structure
A typical neural network has three layers:
- Input Layer
- Hidden Layer(s)
- Output Layer
Each neuron applies a activation function (e.g., ReLU, Sigmoid) to weighted inputs.
📈 Training Process
- Forward Propagation: Input data flows through the network to produce an output.
- Loss Calculation: Compare the output with the actual label.
- Backward Propagation: Adjust weights using gradient descent to minimize error.
For deeper insights, check our tutorial on AI Overview.
🚀 Applications
Neural networks power:
- Image recognition (e.g., CNNs)
- Natural language processing
- Predictive analytics
Add a visual of a deep learning application here:
📚 Further Reading
Explore related topics like Machine Learning Basics or AI Ethics to deepen your understanding.
Note: All images are illustrative and generated for educational purposes.