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Once you've built your model, it's time to train it! You'll feed the training data into the model and let it learn from the images and their corresponding labels. The training process involves calculating the loss, which measures how well the model is performing. You'll then use the optimizer to update the model's weights to reduce the loss. You will usually train your model over a certain number of epochs (cycles). During each epoch, the model will see the entire training dataset once. After each epoch, evaluate the model's performance on the testing data to see how well it's generalizing to new, unseen images. You can also monitor metrics like accuracy and loss to track the progress of your model. After training, you can use the model to predict the digits in the test set. Evaluate the model's performance on the test set using metrics like accuracy, precision, and recall.
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