Table.

Differences between machine learning using traditional algorithms and machine learning using deep neural networks.

	ML	DL
Algorithms	Many different (SVM, DT, kNN, …)	Defined by architecture (RNN, GAN, LSTM, ...)
Data size	Can work well with smaller inputs	Requires large amount of data
Performance	Typically extremely fast	Computational complexity depends on the architecture
Features	Hand-crafted	Can be learned
Preprocessing	Significant effort	Can be trained on raw data
Fine tuning	Setting the algorithm parameters	Can be performed automatically during training
Complexity	Typical simple mathematical models	Depends on the architecture (highly flexible)
Transparency	Typically transparent	Hard to transparently show decision making
Explainability	Typically explainable	Hard to show the reasoning process