I recently started Andrew Ng’s specialization on deep learning and found these two interesting points :

One is about how performance of algorithm changes with the amount of data. Traditional algorithms have limits but Deep neural network has more advantages.

 

Also for the small amount of data traditional algorithms may win over neural nets with good feature engineering.

Second reason is that deep learning requires data, computation and efficient algorithms. Recent years have seen significant advancement in algorithm to increase computation efficiency. For example sigmoid to ReLU was an algorithmic change which allowed gradient to converge faster.

 

Ref : https://www.coursera.org/learn/neural-networks-deep-learning/home