ML vs. DL : What exactly is deep learning?

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 To settle the debate once and for all, in this article I will talk about deep learning and how it is different and/or similar to machine learning. I will start off with machine learning and then connect it to deep learning, eventually establishing how deep learning is a subset of machine learning. 

Machine learning : where it all starts

As you might know already, machine learning or ML is giving machines ability to learn like human beings and for this it uses multiple types of algorithms which are mentioned as follows:
  1. Linear based (regression etc.)
  2. Tree based (decision tree, random forest, xgboost, catboost, etc.)
  3. Neural Network based (ANN, CNN, perceptron, etc)
  4. kNN, etc.
Diagrammatically, it can be shown as follows: 

Machine learning: various algorithms
Machine learning: various algorithms

Machine learning via Neural Network

Now, out of all these various types of algorithms, consider the one using neural networks. This one can further be divided into 2 categories: 
  1. Simple neural networks: meaning there are no hidden layers, and
  2. Deep neural networks: meaning those with (multiple) hidden layers.
The second category above is what constitutes "Deep Learning" which uses neural networks which are deep (meaning there are multiple hidden layers in the neural network).

How deep learning connects with machine learning
What is deep learning?

So, what exactly is Deep Learning?

Deep learning simply means machine learning via the use of deep neural networks (those with multiple hidden layers). As explained above, Deep learning (DL) is a subset (or subcategory) of Machine Learning (ML).

This is also the definition of deep learning and this is how deep learning connects with its parent machine learning. So if anybody asks you the difference between the two, just mention that deep learning is a type of machine learning which specifically uses deep neural networks for the learning process. 

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