How Neural Networks Learn: A Probabilistic Viewpoint

Understanding concepts like entropy, cross-entropy, and KL-Divergence is crucial for training neural networks. These measures help in quantifying similarities or divergences between probability distributions. By interpreting models probabilistically, practitioners can define objective functions — commonly known as loss functions — that need to be minimized during model training, often using gradient descent methods facilitated by frameworks like PyTorch.