feed-forward neural networks figure - the tanh activation function there are many more that can be used for tailored benefits in specified setting or use case notable examples include leaky reluparametric reluand swish good starting point to explore additional activation functions is ackpropagation the most fundamental building block of deep learning is backpropagationshort for backward propagation of errorsan algorithm used for training neural networks in supervised learning though backpropagation was invented in sit was popularized several years laterin by rumelharthintonand williams in their paper "learning representations by back-propagating errors earlierwe studied loss functions that measure the disagreement between the predicted output and the actuals the weights of the network are at first randomly initialized in order for the network to learn (train)the next logical step would be to align the weights such that the