MLP.md (1175B)
1 2 ML D6 3 4 **Definition:** Multilayer perceptrons are a form of deep neural network that are a feedforward process where each output goes forward to the next layer of perceptrons until reaching the output layer. This is a subset of neural networks as not all NNs are fully connected like RNNs/CNNs. 5 6 MLPs can do regression and classification tasks. For regression we need one output for each output feature we would like to predict. With these outputs we can also apply an activation function (default is none), to bound the output range. 7 8 For classification tasks you need to dedicate one output neuron for each class. These classes then use a sigmoid activation function that determines the probability of class membership. To get an output with a sum of 1 (wanted in the case of multiclass classification where only one output is expected) we can use a softmax function for each output. 9 10 For classification tasks with neural networks we generally want to minimize cross entropy rather than MSE which is the normal metric for regression. Cross entropy is the difference between the predicted distribution and the true distribution. This is also used for logistic regression. 11