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## An introduction to artificial neural networks

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In this chapter, an introduction to neural networks (NNs) with an emphasis on classification and regression applications is presented. In this chapter, some preliminaries about natural and artificial neural networks (ANNs) are introduced first. Then, by giving initial concepts about classification and regression problems, appropriate overall structures of ANNs for such applications are explained. The simple structures of NNs and their limitations as well as some more powerful multilayer and deep learning models are introduced in the next part of this chapter. Finally, convolutional NNs and some of their well-known developments are briefly explained.

Chapter Contents:

• 1.1 Introduction
• 1.1.1 Natural NNs
• 1.1.2 Artificial neural networks
• 1.1.3 Preliminary concepts in ANNs
• 1.1.3.1 Mcculloch and Pitz neuron
• 1.1.3.2 Widely used activation functions
• 1.1.3.3 Feedforward and recurrent architectures in ANN
• 1.1.3.4 Supervised and unsupervised learning in ANN
• 1.2 ANNs in classification and regression problems
• 1.2.1 ANNs in classification problems
• 1.2.2 ANNs in regression problems
• 1.2.3 Relation between classification and regression
• 1.3 Widely used NN models
• 1.3.1 Simple structure networks
• 1.3.2 Multilayer and deep NNs
• 1.3.2.1 Multilayer perceptron
• 1.3.2.2 Autoencoders and stacked RBMs
• 1.3.2.3 Convolutional neural networks
• 1.3.2.4 Recurrent neural networks
• 1.4 Convolutional neural networks
• 1.4.1 Convolution layers
• 1.4.2 Pooling layers
• 1.4.3 Learning in CNNs
• 1.4.4 CNN examples
• 1.5 Conclusion
• References

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