Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples.
Loss is the result of a bad prediction. A loss is a number indicating how bad the model's prediction was on a single example.
If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater. The goal of training a model is to find a set of weights and biases that have low loss, on average, across all examples. Higher loss is the worse(bad prediction) for any model.
The loss is calculated on training and validation and its interpretation is how well the model is doing for these two sets. Unlike accuracy, a loss is not a percentage. It is a sum of the errors made for each example in training or validation sets.
In the following diagrams, there are two graphs representing the losses of two different models, the left graph has a high loss and the right graph has a low loss.
Hope this helps!
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