The aim of the book is to give a solid basis in python language applied to data science and Artificial intelligence. In the end, you should be able to program deep architectures using methods recently raised from the AI community and to understand the majority of the scientific articles discussing those themes. In the first part, you will find everyday commands to handle with simple tasks like printing graphs, use vectors or deal with built-in pythonÕs type. In the second part, starting from Fully connected networks, we will cover several topics of machine learning that go from classifications to geometric deep learning till modern tasks like Generative Adversarial Networks. Every new Section will have an introductory theoretical part followed by full implementation in Pytorch, where youÕll see exactly how to put in practice the new knowledge earned.