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@johann

In TensorFlow, an epoch refers to a complete iteration through the entire training dataset. It is a single pass of the entire dataset during model training. In each epoch, the model processes the entire training dataset, calculates the loss function, and updates the model's parameters. The number of epochs is typically determined by the user and is specified before training begins. Training for more epochs allows the model to learn more from the dataset, but it also increases the computational time.

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@johann

Additionally, during the training process, the dataset is often divided into batches, and each epoch consists of multiple iterations (steps) where the model sees and processes a batch of data at a time. This helps in efficient training and optimization of the model, as processing the entire dataset at once can be computationally expensive and memory-intensive. By completing all the epochs, the model can potentially converge to the optimal solution where the loss function is minimized, and the model performs well on unseen data.