Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and credit risk modeling. They have also been used to construct stochastic process models and price derivatives. Despite their usefulness, neural networks tend to have a bad reputation because their performance is “temperamental”. In my opinion, this can be attributed to poor network design owing to misconceptions regarding how neural networks work. This book discusses every aspect of the artificial neural network in very interactive, practical and simple way.