When I first started training deep learning models, I encountered a frustrating problem: my model would achieve amazing accuracy on my training data, but when I tested it on new, unseen data, its performance would be terrible. This common and fundamental issue is known as overfitting. The model hasn’t learned the general patterns in the data; instead, it has effectively memorized the training examples, noise and all.
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Learning to recognize and combat overfitting is one of the most important practical skills in machine learning. This guide will explain what overfitting is and introduce the key techniques I use to prevent it, known as regularization.
📈 What is Overfitting?
I think of overfitting as the difference between truly understanding a subject and just memorizing the answers for a test. A model that overfits performs very well on the data it was trained on because it has learned the specific details and noise of that particular dataset. However, it fails to generalize to new data because it hasn’t learned the underlying, true relationship between the inputs and outputs.
This often happens when a model is too complex for the amount of data it’s being trained on. The model has so much capacity that it can find a way to perfectly fit every single data point, including the random fluctuations, rather than learning the simpler, more general pattern.
🔧 How to Prevent Overfitting: Regularization Techniques
Regularization is a set of techniques designed to prevent overfitting by discouraging the model from becoming too complex. The goal is to help the model generalize better to new data. Here are some of the most common and effective techniques I use:
- Early Stopping: This is the simplest technique. I monitor the model’s performance on a separate validation dataset during training. I stop the training process as soon as the performance on the validation set stops improving, even if the performance on the training set is still getting better. This prevents the model from continuing to learn the noise in the training data.
- Dropout: This is a powerful and widely used technique. During each training step, dropout randomly ‘drops out’ (sets to zero) a certain fraction of the neurons in a layer. This forces the other neurons in the layer to learn more robust features that are not dependent on any single neuron, making the network less likely to overfit.
- L1 and L2 Regularization: These techniques add a penalty to the loss function based on the size of the model’s weights. L2 regularization (also known as weight decay) penalizes large weights, encouraging the model to use smaller, more distributed weights, which tends to result in a simpler, less overfit model. L1 regularization can even push some weights to be exactly zero, effectively performing a form of feature selection.
- A Guide to Generative Adversarial Networks (GANs)
- A Guide to Autoencoders for Dimensionality Reduction
- A Guide to Long Short-Term Memory (LSTM) Networks
- A Guide to Recurrent Neural Networks (RNNs) for Sequential Data
- A Guide to Convolutional Neural Networks (CNNs) for Image Recognition
- A Guide to Backpropagation – How Neural Networks *Really* Learn
- How Neural Networks Learn – A Guide to Loss Functions and Optimization
