done

I learned about the Single-Layer Network and Multi-Layer Network. 1. Single-Layer Network: we have only Input and Output layers, [NO HIDDEN LAYERS]. 2. Multi-Layer Network: We have an Input layer, then in middle, we have HIDDEN Layer/Layers, and then we have the Output layer.Main difference is there is no Hidden Layer in Single layer Network but in Multi-Layer we have Hidden Layers.

Single layer network doesn’t have hidden layer, multi layer network has hidden layers.

Done

learned single layer vs multi layer neural network

I learned single-layer vs multi-layer neural networks in the context of complexity, learning capability, activation function, applications, flexibility, training method, and it examples

I learned in this video about single-layer (Input layer and Output layer) and Multi-layer (Input layer, Hidden layer, and Output layer).

I learned in this lecture about single-layer (input layer and output layer ) and multi-layer (input layer, hidden layer, and output layer).