fitcnet and patternnet can both be used to solve tabular classification problems.
patternnet is used to define a network architecture which can then be passed to the train function, along with training data, to train a network. fitcnet defines the network architecture and trains the network based on training data in a single line of code.
There are some differences between the two approaches. For example, fitcnet uses the L-BFGS optimizer to train the model. patternnet defaults to the scaled conjugate gradient optimizer -- though others are available. In addition, the ClassificatioNeuralNetwork object returned by fitcnet has properties and methods common to the other fitc* functions for tabular classification -- for example predict, loss and edge.
Finally, note that fitcnet is available in the Classification Learner app, which facilitates easy comparison of multiple machine learning models for tabular classifcation problems.
