January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Forward and backward propagation
Let me show you how forward pass and backward pass work with the help of an example.
We have got a network that has got two layers (1 hidden layer and 1 output layer). Every layer (including the input) has got two nodes. It has got bias nodes as well, as shown in the following diagram:
The notations that are used in the preceding diagram are as follows:
- IL: Input layer
- HL: Hidden layer
- OL: Output layer
- w: Weight
- B: Bias
We have got the values for all of the required fields. Let's feed this into the network and see how it flows. The activation function that's being used here is the sigmoid.
The input that's ...
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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.