In Chapter 2, we briefly touched on the topic of neural networks in our exploration of the machine learning landscape. A neural network is a set of equations that we use to calculate an outcome. They aren’t so scary if we think of them as a brain made out of computer code. In some cases, this is closer to reality than we should expect from such a cartoony example. Depending on the number of features we have in our data, the neural network almost becomes a “black box.” In principle, we can display the equations that make up a neural network, but at a certain level, the amount of information becomes too cumbersome to intuit easily.

Neural networks are used far and wide in industry largely due to their accuracy. Sometimes, there are trade-offs between having a highly accurate model, but slow computation speeds, however. Therefore, it’s best to try multiple models and use neural networks only if they work for your particular dataset.

In Chapter 2, we looked at the development of an AND gate. An AND gate follows logic like this:

`x1`

`<-`

`c`

`(`

`0`

`,`

`0`

`,`

`1`

`,`

`1`

`)`

`x2`

`<-`

`c`

`(`

`0`

`,`

`1`

`,`

`0`

`,`

`1`

`)`

`logic`

`<-`

`data.frame`

`(`

`x1`

`,`

`x2`

`)`

`logic`

`$`

`AND`

`<-`

`as.numeric`

`(`

`x1`

`&`

`x2`

`)`

`logic`

`## x1 x2 AND`

`## 1 0 0 0`

`## 2 0 1 0`

`## 3 1 0 0`

`## 4 1 1 1`

If you have two 1 inputs (both TRUE), your output is 1 (TRUE). However, if either of them, or both, are 0 (FALSE), your output is also 0 (FALSE). This computation is somewhat similar to our analysis of logistic regression. In ...

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