I'm currently studying deep learning using the textbook Deep Learning (Goodfellow, Bengio, & Courville - 2015) and had a question regarding a concept of machine learning provided in the book.
Specifically, the sentence I read is on page 115 under 5.2. Regularization and states as follows:
The no free lunch theorem implies that we must design our machine learning algorithms to perform well on a specific task.
I'm a bit confused by this statement. My understanding of the NFL theorem is that if we average each algorithm's performance over every existing sample, then their performances are pretty much the same.
If my understanding is correct, doesn't the above statement contradict the NFL theorem because it's implying that an algorithm performs better than another at a given task?