What are the advantages and disadvantages of Deep Learning?


  • Often unbeatable in terms of accuracy (by the Universal Approximation Theorem, under general conditions, there is guaranteed to exist some network that can approximate any continuous function).  
  • Well-suited to unstructured data problems (text, image classification, computer vision) that is not the case for many other classes of machine learning algorithms


  • More prone to overfitting that most other statistical or machine learning approaches
  • Many hyperparameters to tune
  • Black box interpretation (almost impossible to understand what is happening in hidden layers within deep networks)
  • May require massive amounts of data to be able to sufficiently learn a dataset