defining layers and neurons, enabling users to construct complex network architec- tures. • Backpropagation: The implementation of backpropa- gation in micrograd++ allows for efficient training of models following code snippet demonstrates the basic usage of micrograd++ for performing operations and backpropagation on scalar values. # include # include ” Value . hpp ” i n t main ( ) { auto a = multi-layer percep- tron, composed of multiple layers. It supports the training of models using backpropagation and gradient descent, allowing for the efficient optimization of network parameters. VI. SUPPORTING