May 2020
Intermediate to advanced
530 pages
17h 8m
English
Content preview from Hands-On Machine Learning with C++
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The linear activation function
The linear activation function, y = c x, is a straight line and is proportional to the input (that is, the weighted sum on this neuron). Such a choice of activation function allows us to get a range of values, not just a binary answer. We can connect several neurons and if more than one neuron is activated, the decision is made based on the choice of, for example, the maximum value.
The following diagram shows what the linear activation function looks like:
The derivative of y = c x with respect to x is c. This conclusion means that the gradient has nothing to do with the argument of the function. The gradient ...
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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.