Chapter 11 is among the most interesting chapters for deep learning practitioners that already have some background on the involved theory and algorithms. What Goodfellow et al. call "Practical Methodology" can best be described as a loose set of tips and tricks for approaching deep learning problems. They give the following, general process that should be followed:
Surprisingly, this approach has many parallels with modern, agile software engineering principles (e.g. prototyping, iterative development, risk focus).
Goodfellow et al. then discuss some of these aspects in detail. The most interesting points are made on diagnosing a running end-to-end system:
What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: