In this chapter, we have introduced neural networks in detail and we have mentioned their success over other competing algorithms. Neural networks are comprised of the "units", or neurons, that belong to them or their connections, or weights, that characterize the strength of the communication between different neurons and their activity functions, that is, how the neurons process the information. We have discussed how we can create different architectures, and how a neural network can have many layers, and why inner (hidden) layers are important. We have explained how the information flows from the input to the output by passing from each layer to the next based on the weights and the activity function defined, and finally we have shown how we can define a method called back-propagation to "tune" the weights to improve the desired level of accuracy. We have also mentioned many of the areas where neural networks are and have been employed.
In the next chapter, we will continue discussing...