November 2017
Intermediate to advanced
304 pages
6h 58m
English
Content preview from Machine Learning with TensorFlow 1.x
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Fulfilment
Likely, by now, you must have many questions running through your mind—why three convolutional layers rather than two or four? Why a stride of one? Why a patch size of five? Why end up with fully connected layers rather than start with them?
There is some method to the madness here. At the core, CNN's are built around image processing and patches are built around the features being sought. Why some configurations work well while others do not is not fully understood, though general rules do follow intuition. The exact network architectures are discovered, honed, and increasingly inch toward perfection through thousands of trials and many errors. It continues to be a research-grade task.
The practitioner's general approach is often ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.