👉 Webreps are a technique used in machine learning where the weights of hidden layers in a neural network are learned by computing their cross-entropy loss function on a small training data subset. This approach has been shown to improve model performance significantly over traditional backpropagation methods for supervised learning tasks, such as image classification and natural language processing. The idea behind webreps is to use the difference between predicted values from multiple hidden layers in a neural network and the ground truth values to train the weights of these layers. This method allows us to learn weights that are better suited for handling more complex relationships or dependencies between features, which can lead to improved model performance on real-world tasks. The term webreps is also commonly used to describe this technique in machine learning, especially within the context of neural networks and deep learning.
