A Key to Better Neural Networks
Dropout is a technique in neural networks where random neurons are ignored during training, helping prevent overfitting and making models more robust.
Dropout stops neural networks from memorizing training data. This helps the model learn real patterns and improves accuracy on new, unseen data.
During training, a set percentage of neurons are "dropped out" at random. This means they don’t participate in that training step.
The dropout rate is a key setting. Common values are 0.2 to 0.5, meaning 20–50% of neurons are randomly dropped during each training pass.
Dropout acts like training many smaller networks at once. At test time, all neurons are used, leading to a stronger, more general model.
Dropout reduces overfitting, improves generalization, and encourages neurons to learn independently-making your neural network more reliable.
Dropout is easy to add in frameworks like PyTorch or TensorFlow. Tune the dropout rate for best results and always turn it off during inference