A neural network can be considered as a networked set of logistic regression units.

While a single logistic regression can perform as a classifier on it's own it's not suited for problems where input dimensions are very high and your data is not linearly separable.

By using multiple such units, a neural network attempts to approximate any given function. The more important aspect in using a neural network however is in knowing how to train each of these units.

Here's another question similar to yours: What is the difference between logistic regression and neural networks?

A neural network can be considered as a networked set of logistic regression units.

While a single logistic regression can perform as a classifier on it's own it's not suited for problems where input dimensions are very high and your data is not linearly separable.

By using multiple such units, a neural network attempts to approximate any given function. The more important aspect in using a neural network however is in knowing how to train each of these units.

Here's another question similar to yours: What is the difference between logistic regression and neural networks?