February 2018
Intermediate to advanced
450 pages
11h 27m
English
Content preview from Deep Learning By Example
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Linear regression with TensorFlow
For the first part, we will generate random data points and define a linear relation; we'll use TensorFlow to adjust and get the right parameters:
input_values = np.random.rand(100).astype(np.float32)
The equation for the model used in this example is:
Nothing special about this equation, it is just a model that we use to generate our data points. In fact, you can change the parameters to whatever you want, as you will do later. We add some Gaussian noise to the points to make it a bit more interesting:
output_values = input_values * 2 + 3output_values = np.vectorize(lambda y: y + np.random.normal(loc=0.0, ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.