Deep Neural Network vs Sparse Neural Network || Benefits of using Sparse Neural Network||
A Deep Neural network is one where all the neurons are fully connected to each other, while in case of Sparse Neural network connection is sparse. Sparse neural networks have gained attention due to their potential benefits in terms of model efficiency and interpretability.
While creating a neural network, with two options at our side, let us decipher which one is suitable to us based on different factors like, amount of data, resource constraints and interpretability.
When we have abundance of data.
In a situation where we have abundance of data , it simply means a large amount of training data, dense network is good option as they can effectively learn from it , and it opens the scope of enormous parameter tuning according to our need.
When we have resource constraint.
In situation where we have limited computational resources, sparse networks can be advantageous as they require fewer parameters and can be faster to train and deploy. Sparse neural networks are commonly used in areas where model efficiency and interpretability are crucial, such as edge computing, IoT devices, and applications with strict latency constraints.
When we have high dimension data.
In a situation where each input feature is releavent and carries meaningful information, Dense neural network is go to option.Sparse neural networks, with fewer connections (e.g., using techniques like pruning or sparsity-inducing regularization), can be beneficial when we want to reduce the complexity of our model and improve its interpretability without compromising efficiency.
conclusion:
In real world while choosing which one to use dense or sparse, it requires alot of testing and mathematical understanding. But, here are some standard scenarios which gives us an idea of suitability of algorithm in a standard situation. In this article we uncovered basic scenarios of when to utilises DNN and SNN. I hope you liked the article. Happy reading :).
