Principles of artificial neural networks : basic designs to deep learning Introduction and role of artificial neural networks Fundamentals of biological neural networks Basic principles of ANNs and their structures The perceptron The madaline Back propagation Hopfield networks Counter propagation Adaptive resonance theory The cognitron and neocognition Statistical training Recurrent (time cycling) back propagation networks Deep learning neural networks : principles and scope Deep learning convolutional neural network LAMSTAR neural networks Performance of DLNN : comparative case studies.