How do channels differ from layers?

Channels and layers serve different purposes in neural networks. Layers refer to the distinct levels of computation within the network, such as input, hidden, and output layers, where each layer consists of neurons performing transformations on the data. Channels, on the other hand, typically refer to the different feature maps or dimensions within a layer, especially in convolutional neural networks, where they represent various aspects of the input data, such as color channels in images or feature representations. Essentially, layers are structural components of the network, while channels are specific data representations within those layers.