train/train_loss (22/12/16 11:22:15)
Created on December 16|Last edited on March 22
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Training loss in deep learning is a metric used to evaluate the performance of a neural network during the training process.
It measures how well the network is able to predict the correct output for a given input, compared to the actual output.
The goal is to minimize the training loss, which means the network is becoming more accurate in its predictions. This is typically achieved through techniques such as backpropagation and gradient descent.
You can see in the visualization above that the model is getting more and more accurate.
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