Regularization techniques, like L1 and L2 regularization, can really help improve how activation functions work in deep learning.
They do this by stopping overfitting, which happens when a model learns too much from the training data. Regularization adds a small penalty to the loss function based on the model's weights, which keeps everything in check.
Better Generalization: Regularization encourages smaller weights. This means the model is simpler and does a better job when it sees new data.
Smoother Activation Responses: By keeping the weights from growing too big, the activation functions work better. This makes the process of finding the best model more efficient.
For example, let’s look at a model that uses the ReLU activation function. When L2 regularization is added, the model is less likely to react too much to noise in the training data. This helps it perform better when it’s tested on new data.
Regularization techniques, like L1 and L2 regularization, can really help improve how activation functions work in deep learning.
They do this by stopping overfitting, which happens when a model learns too much from the training data. Regularization adds a small penalty to the loss function based on the model's weights, which keeps everything in check.
Better Generalization: Regularization encourages smaller weights. This means the model is simpler and does a better job when it sees new data.
Smoother Activation Responses: By keeping the weights from growing too big, the activation functions work better. This makes the process of finding the best model more efficient.
For example, let’s look at a model that uses the ReLU activation function. When L2 regularization is added, the model is less likely to react too much to noise in the training data. This helps it perform better when it’s tested on new data.