-	It is helpful to plot curves showing the relationship between training set size and generalization error. By extrapolating such curves, one can predict how much additional training data would be needed to achieve a certain level of performance. 
-	It is recommended to experiment with training set sizes on a logarithmic scale, for example, doubling the number of examples between consecutive experiments.


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-	First, determine whether the performance on the training set is acceptable. If performance on the training set is poor, the learning algorithm is not using the training data that is already available, so there is no reason to gather more data. Instead, try increasing the size of the model by adding more layers or adding more hidden units to each layer.
-	Try improving the learning algorithm (tuning the learning rate hyperparameter). 
-	If large models and carefully tuned optimization algorithms do not work well, then the problem might be the quality of the training data. This suggests starting over, collecting cleaner data, or collecting a richer set of features.
-	If the performance on the training set is acceptable, then measure the performance on a test set. If test set performance is much worse than training set performance, then gathering more data is one of the most eﬀective solutions. 
-	A simple alternative to gathering more data is to reduce the size of the model or improve regularization, by adjusting hyperparameters such as weight decay coeﬃcients, or by adding regularization strategies such as dropout.


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