Epoch in Neural Network
What is an Epoch in Neural Networks?
An epoch is one complete pass through the entire training dataset by the neural network.
Think of it like this: We are trying to memorize a list of 100 questions and answers for a test.Every time we read through the full list once, that’s one epoch.
Why Do We Need Multiple Epochs?
One pass (epoch) is usually not enough for the neural network to learn well. It needs repetition to improve. Each epoch helps the network get better at predicting by updating its internal rules (weights).
2. Advantages of Using Epochs:
| Advantage | Explanation |
|---|---|
| Better Learning | The more times the network sees the data, the more it can learn patterns. |
| Weight Optimization | Each epoch helps in adjusting weights a bit to reduce prediction error. |
| Improves Accuracy | Gradual improvement after every epoch – similar to practicing more. |
| Error Reduction | Over epochs, the model’s loss (error) keeps going down (ideally). |
Visual Analogy:
Imagine drawing a curve with shaky hands…
- 1st try (Epoch 1): It’s way off.
- 2nd try (Epoch 2): Slightly better.
- 10th try (Epoch 10): Much smoother and more accurate!
That’s how a neural network gets better through epochs.
Epoch in Neural Network – Epoch example with Simple Python