What is epochs?
An epoch is one complete pass through the entire training dataset by a machine‑learning algorithm. Imagine you have a stack of flashcards (your data); looking at every card once before starting over is one epoch.
Let's break it down
- Dataset: All the examples you use to teach the model (images, text, numbers, etc.).
- Pass: Going through each example one after another.
- Epoch: When you have finished that pass, you have completed one epoch.
- After each epoch, the model usually updates its internal parameters (weights) based on what it learned.
Why does it matter?
The number of epochs determines how long the model trains. Too few epochs → the model may not learn enough (underfitting). Too many epochs → it may memorize the training data and perform poorly on new data (overfitting). Finding the right number helps the model generalize well.
Where is it used?
Epochs are a core concept in virtually all supervised learning tasks:
- Image classification (e.g., recognizing cats vs. dogs)
- Natural language processing (e.g., sentiment analysis)
- Speech recognition
- Any deep‑learning project that uses gradient‑based optimization.
Good things about it
- Control: You can decide how many times the model sees the data, giving you fine‑grained control over training.
- Progress tracking: By evaluating performance after each epoch, you can see if the model is improving.
- Flexibility: You can combine epochs with techniques like early stopping, learning‑rate schedules, or data augmentation.
Not-so-good things
- Time‑consuming: More epochs mean longer training times, especially with large datasets.
- Risk of overfitting: Training for too many epochs can cause the model to memorize noise instead of learning patterns.
- Diminishing returns: After a certain point, additional epochs may give only tiny accuracy gains, wasting resources.