I’m a layperson trying to understand how neural networks work. I know (if this is correct) that training begins with the weights and biases being random. Then as training progresses these parameters are tuned. The loss function triggers back propagation that further tunes the parameters. I know that the neurons, or sets of neurons come to act like detectors (edge, color, etc, if it’s a visual system). I’m wondering if during the tuning and the training is teaching the net how to classify animals for example, do the weights and biases of these ‘detector’ nodes get tweaked? Or, instead, is it some other type of node which gets tweaked, like a ‘classifier’ node? In short, is there a hierarchy of nodes, like detectors at the bottom and classifiers above, and if so, does training tweak all the nodes or just the higher level classes of nodes? Apologies for any lack of rigor in my use of the terminology! Thx. submitted by /u/Electrical-Size-5002
Originally posted by u/Electrical-Size-5002 on r/ArtificialInteligence