Hmm do you know about dense and sparse neural network

A dense neural network is like a system where every neuron talks to every other neuron in the next layer. It learns a lot because it has tons of connections, but that also makes it heavy, slow, and expensive to train—especially in big models like those used in computer vision or large language models. It uses a lot of memory and processing power. A sparse neural network, on the other hand, keeps only the important connections and removes the rest. Many weights become zero, so the model becomes much lighter and faster. This is usually done through pruning, where unimportant connections are cut off. Sparse networks use less memory and compute resources while still keeping similar performance. That’s why sparsity is super important today, especially when running big models on smaller devices like phones or edge devices. In short, dense = full connections and heavy, while sparse = fewer connections, faster, and more efficient, trying to give the same results with much fewer computations.