Learning Rate adjustment Impact in Neural Network
1. What is Learning Rate in Neural Networks?
Imagine we’re learning to hit a target with a bow and arrow. On our first shot, we miss. We now adjust our aim based on how far we missed:
- If we adjust too little, it’ll take forever to get closer.
- If we adjust too much, we might overshoot every time.
Learning Rate is like this adjustment step size in a neural network.
- A small learning rate means our network learns slowly — very precise, but can take too long or get stuck.
- A large learning rate means our network learns fast — but might skip over the best solution or never settle down.
2. How Does Learning Rate Impact Neural Network?
| Learning Rate | Impact on Learning |
|---|---|
| Too Small | Very slow training, might get stuck |
| Just Right | Efficient learning, finds the minimum |
| Too Large | Oscillates or diverges, fails to learn |
Learning Rate adjustment Impact in Neural Network – Learning Rate Adjustment Impact example with Simple Python