What does "Wide" vs. "Deep" mean in the context of Neural Networks? From this article, I read that "to accurately classify data with neural networks, wide layers are sometimes necessary."

However, I have seen many implementations and discussions on deep-learning, such as this, mention the concept of depth.  

What is the difference in the context of neural networks?  How does width vs depth impact a neural network's performance?
The width refers to the number of neurons in a layer. The depth refers to the number of layers. 

Have a look at the following question regarding the impact of these hyper-parameters on the performance of the neural network: https://stats.stackexchange.com/q/214360/82135.