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Machine Learning For Dummies
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Given the neural network architecture, you can imagine how easily the algorithm could learn almost anything from data, especially if you added too many layers. In fact, the algorithm does so well that its predictions are often affected by a high estimate variance called overfitting. Overfitting causes the neural network to learn every detail of the training examples, which makes it possible to replicate them in the prediction phase. But apart from the training set, it won’t ever correctly predict anything different. The following sections discuss some of the issues with overfitting in more detail.
When you use a neural network for a real problem, you have to take some cautionary steps in a much stricter way than you do with other algorithms. Neural networks are frailer and more prone to relevant errors than other machine learning solutions.
First, you carefully split your data into training, validation, and test sets. Before...
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