by Michele Laurelli
A regularization technique that randomly deactivates neurons during training to prevent overfitting.
During each training iteration, dropout temporarily removes a random percentage of neurons. This forces the network to learn more robust and generalizable representations.
Dropout with rate 0.5 in dense layers
Reducing overfitting in deep networks
Improving generalization
When a model learns training data too well, including noise, resulting in poor generalization to new data.
Techniques to prevent overfitting by adding constraints or penalties to the model during training.
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) that process information.