Artificial neural networks are computer systems that are inspired by biological neural networks. These systems are interconnected network nodes ("neurons") that work together to produce an output function. Similar to biological neural networks, artificial neural networks 'learn' to perform certain tasks by considering examples. For centuries, scientists have combed through data by hand in an attempt to find meaningful patterns to solve complex problems. Recent advances in computing power enable the application of deep learning in all fields of knowledge. deep learning can involve interpreting data, recognizing patterns, and making decisions. The application and acceptance of this method in the fields of biology and biomedicine is reaching its peak. Rapid development and solving complex problems in a fraction of the time has the effect of reducing the cost of laboratory supplies and equipment that is alternatively required for conducting research. In biology as well as in other scientific fields, complex and highly variable conditions are the norm. The application of deep artificial neural networks through learning here represents a valuable tool that reduces systematic, hypothesis-driven approaches in research to simpler linear relationships. Today's exponential growth in the application of deep learning can be compared to the Cambrian explosion of half a billion years ago, when life on Earth experienced a short period of very rapid diversification. A shift of this scale, the fusion of previous knowledge with the possibilities of machine learning is a marking step towards the revolutionization of science, including the fields of biology and biomedicine.