Summary
In this chapter, we have explored some basic concepts and ideas that lie at the foundation of machine learning. And we haven’t just explored them from a theoretical point of view: we have also seen them come to life.
We have learned what machine learning is all about, and we have discussed some of the most common approaches used to make it a reality. In particular, we have learned that many machine learning problems can be reduced to the minimization of a loss function through some optimization algorithm on a suitable model.
We have also studied in some depth classical neural networks, and we have used an industry-standard machine learning framework (TensorFlow) to train one.
Lastly, we have wrapped up this chapter by introducing what quantum machine learning is all about and having a sneak peek into the rest of the chapters of this part of the book.
