In machine learning, an epoch is a single complete pass through the entire training dataset by a learning algorithm. During an epoch, the model processes each training example once, updating its parameters according to the learning method being used (such as gradient descent). The concept of an epoch is especially important in supervised learning and neural network training, where optimizing model performance often requires multiple passes through the data.
When you train a model, you rarely achieve optimal performance in just one epoch. Instead, training typically involves running many epochs, allowing the model to gradually learn patterns and minimize error. After each epoch, the model has ideally improved its predictions based on the feedback from the previous pass. This iterative process is crucial for convergence, where the model‘s loss (a measure of prediction error) stabilizes or reaches its minimum.
It’s important not to confuse an epoch with a batch or an iteration. A batch is a subset of the training data processed before the model’s parameters are updated. When the dataset is too large to fit into memory, it’s divided into batches. An iteration refers to a single update of the model’s parameters, which happens after processing a batch. Therefore, the number of iterations per epoch equals the total number of batches.
For example, if you have 10,000 training examples and use a batch size of 100, one epoch will consist of 100 iterations (10,000 divided by 100). If you train for 10 epochs, your model will see each training example 10 times, for a total of 1,000 iterations.
Choosing the right number of epochs is a key part of the training process. Too few epochs can result in underfitting, where the model hasn’t learned enough from the data. Too many epochs can lead to overfitting, where the model learns the training data too well, including its noise, and performs poorly on new, unseen data. Techniques like early stopping monitor performance on a validation set and halt training when improvement stalls, helping to avoid overfitting.
The concept of epochs is relevant across many machine learning frameworks and models, from simple linear regression to complex deep neural networks. Understanding how epochs interact with other training parameters, such as learning rate and batch size, can help practitioners train more effective and efficient models.
In summary, an epoch is a fundamental unit in the machine learning training process, representing one full pass through the training data. Mastering how to use and tune epochs, along with related concepts like batch size and iterations, is essential for successful model training.
