I found this very awesome playground for visualizing the output of a neural network
I try to understand how the neural network works with basic dataset:
- for circle dataset, it is possible to build a nnet with 2 features ($X_1^2$ and $X_2^2$) and 0 hidden layer.
- for exclusive or dataset, we can use the $X_1X_2$ feature and 0 hidden layer
- for gaussian dataset, we can also use $X_1$ and $X_2$ features and 0 hidden layer
It is possible to obtain the "good" solutions with this above. Now when it comes to spiral dataset, I don't know how to find a simple solution.
How could you explain, with a particular dataset, the importance of hidden layers? For example, for dataset (except spiral dataset), what is your suggestion to mitigate the predictions and why?