Five different techniques to prevent overfitting:
Early Stopping: In this method, we track the loss on the validation set during the training phase and use it to determine when to stop training such that the model is accurate but not overfitting.
Image Augmentation: Artificially boosting the number of images in our training set by applying random image transformations to the existing images in the training set.
Dropout: Removing a random selection of a fixed number of neurons in a neural network during training.
Increase dataset size: The more training data you feed, the less likely it is to overfit. The reason is that, as you add more data, the model cannot overfit all the samples, and is forced to generalize to make progress.
Regularization: Regularization is a method to constrain the model to fit our data accurately and not overfit. It can also be thought of as penalizing unnecessary complexity in our model
Did I miss anything?🤔 Let me know in the comments. Happy Learning.😊
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