Thesis topic focuses on Machine Learning from the software point of view, nowadays one of the research route for the management of large databases. Machine Learning is already widely present in our daily lives and we can find it, for example, both in the anti-spam filter of electronic mail, and in facial recognition of cameras, in the automatic corrector of smartphones, or in weather forecasts, etc. The aim of the thesis is to review algorithms written in Python language for models robust to adversarial attacks and try to apply to them fast training techniques to improve computational time. The word “fast training” refers to a code able to reach the skill to distinguish and divide a large database's data in a reasonable time according to the learning rules given by the programmer.