Dropout and batch normalization are both important for improving a model's accuracy, but they work in different ways:
Dropout: This is a method that helps stop overfitting. It randomly "drops" some neurons (or parts of the model) while training. This makes the model learn stronger and better features.
Batch Normalization: This process adjusts the inputs to each layer of the model. It helps the training go faster and stay stable. This often leads to better accuracy and lets the model learn at a quicker pace.
In real-life use, combining both dropout and batch normalization can lead to even better results!
Dropout and batch normalization are both important for improving a model's accuracy, but they work in different ways:
Dropout: This is a method that helps stop overfitting. It randomly "drops" some neurons (or parts of the model) while training. This makes the model learn stronger and better features.
Batch Normalization: This process adjusts the inputs to each layer of the model. It helps the training go faster and stay stable. This often leads to better accuracy and lets the model learn at a quicker pace.
In real-life use, combining both dropout and batch normalization can lead to even better results!