Summary
We got the chance to go deep into DL in this chapter and understand some of the major influences that impact this subsection of ML. We also got the chance to look at some of the specific ANNs that are most commonly used in products powered by DL in order to get more familiar with the actual models we might come across as we build with DL. We ended the chapter with a look at some of the other emerging technologies that collaborate with DL, as well as getting further into some of the concepts that impact DL most: explainability and guidelines for success.
DL ANNs are super powerful and can exhibit great performance, but if you need to explain them, you will run into more issues than you would if you stick to more traditional ML models. We’ve now spent the first three chapters of the book getting familiar with the more technical side of AI product management. Now that we’ve got that foundation covered, we can spend some time contextualizing all this tech.
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