Further reading

To learn more about the topics that were covered in this chapter, please take a look at the following references:

• Ruiz, N. W. (2020, December 22). The Future is Federated. Medium.
• Contessa, G. (2010). Scientific Models and Fictional Objects. Synthese, 172(2): 215–229.
• Frigg, R. and Hartmann, S. (2020). Models in Science. In Zalta, E. N. (ed.). The Stanford Encyclopedia of Philosophy.
• Bunge, M. (1963). A General Black Box Theory. Philosophy of Science, 30(4): 346-358.
• Moore, S. K., Schneider, D. and Strickland, E. (2021, September 28). How Deep Learning Works: Inside the neural networks that power today’s AI. IEEE Spectrum.
• Raschka, S. and Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 (3rd Ed.). Birmingham: Packt Publishing.
• McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2016). Communication-efficient learning of deep networks...