Understanding Epoch, Batch, and Iteration in Deep Learning Training Process

 In deep learning, the terms epoch, batch, and iteration are often used to refer to different aspects of the training process. Here are the differences between them:

1. Epoch: An epoch refers to a complete iteration through the entire training dataset. In other words, one epoch means that the model has seen every training example once.

2. Batch: A batch refers to a subset of the training data that is processed at once. Instead of feeding all the training data to the model at once, it is divided into small batches, and the model is trained on each batch sequentially. The size of the batch is typically a hyperparameter that is set before training.

3. Iteration: An iteration refers to the number of batches that are processed by the model during training. For example, if we have 1000 training examples and set a batch size of 100, it will take 10 iterations to complete one epoch. In other words, one iteration means that one batch of data has been processed by the model.

To summarize, an epoch represents one full pass through the entire dataset, while a batch is a smaller subset of the data used for training. Iterations represent the number of times the model processes a batch of data during the training process.

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