July 2019
Intermediate to advanced
512 pages
19h 39m
English
Content preview from Hands-On Deep Learning Algorithms with Python
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Defining the loss function
The mean squared error (MSE) of regression is given as follows:
Here, 
is the number of training samples, 
is the actual value, and 
is the predicted value.
The implementation of the preceding loss function is shown here. We feed the data and the model parameter, theta, to the loss function, which returns the MSE. Remember ...
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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.