DropOut and Monte Carlo Dropout (MC Dropout)- Day 48

Understanding Dropout in Neural Networks Understanding Dropout in Neural Networks with a Real Numerical Example In deep learning, overfitting is a common problem where a model performs extremely well on training data but fails to generalize to unseen data. One popular solution is dropout, which randomly deactivates neurons during training, making the model more robust. In this section, we will demonstrate dropout with a simple example using numbers and explain how dropout manages weights during training. What is Dropout? Dropout is a regularization technique used in neural networks to prevent overfitting. In a neural network, neurons are connected between layers, and dropout randomly turns off a subset of those neurons during the training phase. When dropout is applied, each neuron has a probability \( p \) of being “dropped out” (i.e., set to zero). For instance, if \( p = 0.5 \), each neuron has a 50% chance of being dropped for a particular training iteration. Importantly, dropout does not remove neurons or weights permanently. Instead, it temporarily deactivates them during training, and they may be active again in future iterations.   Let’s walk through a numerical example to see how dropout works in action and how weights are managed…