Tune Hyperparameters
Explore how adjusting neural network hyperparameters such as the number of training epochs and hidden nodes affects model accuracy. Learn to tune these settings one at a time to find the optimal balance between training time and performance, helping you build more effective machine learning models.
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We built a neural network and prepared its input data, but that was only the beginning. The same algorithm running on the same data can yield wildly different results, depending on hyperparameters such as the learning rate and the number of hidden nodes.
ML development is mostly about finding good values for those hyperparameters. Compared to software development, there is no hard-and-fast rule that tells us how to set those hyperparameters.
We should not change multiple hyperparameters at the same time. Otherwise, we would not know which changes affected the network’s accuracy. Instead, let’s tune those hyperparameters one at a time, starting with the easy one.
Picking the number of epochs
The easiest hyperparameter to tune is epochs arguably. We already know that
the longer we train a system, the more accurate it becomes—up to a point.
After a ...