What is the difference between steps and epochs in TensorFlow?

Answer: In TensorFlow, a step refers to one optimization update, while an epoch corresponds to one complete pass through the entire training dataset.

let’s delve into more detail and present the differences between steps and epochs in TensorFlow through a table:

Aspect	Steps	Epochs
Definition	A step is one optimization update, where the model parameters are adjusted based on a subset (batch) of the training data.	An epoch is a complete pass through the entire training dataset, during which the model sees and learns from all the training examples.
Number of Updates	Multiple steps can be executed within a single epoch, depending on the batch size (total number of examples processed per step).	Only one epoch is completed after the model has processed all training examples once.
Impact on Learning	More steps allow for more frequent parameter updates, potentially leading to faster convergence and fine-grained adjustments.	Completing more epochs gives the model more opportunities to learn from the entire dataset, capturing a broader understanding of the data.
Control in Training	You can manually set the number of steps, and training stops when the specified number is reached.	You can set the number of epochs, and training stops when the specified number is completed.
Use Case	Commonly used in scenarios with large datasets where processing the entire dataset in one go is not feasible, allowing for efficient updates.	Useful when you want the model to see the entire dataset multiple times, ensuring comprehensive learning.

Conclusion:

In summary, steps are individual optimization updates based on batches of data, while epochs involve going through the entire dataset. The choice between steps and epochs depends on the specific requirements of the machine learning task and the characteristics of the dataset.