November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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Understanding categorical cross entropy loss
Cross entropy loss, or log loss, measures the performance of the classification model whose output is a probability between 0 and 1. Cross entropy increases as the predicted probability of a sample diverges from the actual value. Therefore, predicting a probability of 0.05 when the actual label has a value of 1 increases the cross entropy loss.
Mathematically, for a binary classification setting, cross entropy is defined as the following equation:
Here, 
is the binary indicator (0 or 1) denoting ...
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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.