The previous chapter introduced feed-forward neural networks and demonstrated that, theoretically, implementing the training procedure for an arbitrary feed-forward neural network is relatively simple. Unfortunately, neural networks trained this way will suffer from several problems such as stability of the training process – that is, slow convergence due to parameters jumping around a good minimum – and overfitting. In this chapter, we will describe several practical solutions that mitigate these problems. In particular, we discuss minibatching, multiple optimization algorithms, other activation and cost functions, regularization, dropout, temporal averaging, and parameter initialization and normalization.