In this book, the reader will find efficient methods for optimizing neural networks. Particularly through the detailed treatment of reinforcement learning and evolutionary optimization, it goes beyond the content of previous textbooks on the subject of neural networks. The optimization of neural models is divided into the following factors: neural models, learning problems, optimization methods, and hardware implementation. This allows the reader to combine the building blocks according to their goals in their own application: neural model vs. neuro-fuzzy model, supervised learning vs. reinforcement learning, gradient descent vs. evolution, sequential vs. parallel.