# How to understand Neural network (with tensorflow)? [closed]

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?

## closed as too broad by mkt, Michael Chernick, kjetil b halvorsen, StatsStudent, SycoraxJun 19 at 13:15

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

(While the circle data is non-linear, the features $$X^2_1$$ and $$X^2_2$$ allow the logistic regression to classify the data correctly.)