In this chapter, we looked at what artificial neural networks are, looked at their history, and examined the reasons for their appearance, rise, and fall and why they have become one of the most actively developed machine learning approaches today. We looked at the difference between biological and artificial neurons before learning the basics of the perceptron concept, which was created by Frank Rosenblatt. Then, we discussed the internal features of artificial neurons and networks, such as activation functions and their characteristics, network topology, and convolution layer concepts. We also learned how to train artificial neural networks with the error backpropagation method. We saw how to choose the right loss function for different types of tasks. Then, we discussed the regularization methods that are used to combat overfitting during training.
Finally, we implemented...