Machine Learning vs. Deep Learning



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Machine Learning, instead, does not have any characteristic architecture. Several different algorithms, with different tasks, can be classified as deep learning.

Deep Learning has two or more layers of neurons. Therefore, all algorithms built using a neural network architecture are classified as deep learning. All the other algorithms are classified as Machine Learning.

Visualization of the difference between Machine Learning (ML) and Deep Learning (DL)

Their algorithms mix

In reality, there are algorithms that solve identical tasks that Machine Learning solves, such as Regression or Classification problems but have a partial or a total architecture. Some examples are Multilayer Perceptrons, which use only Deep Learning to solve both Regression and Classification problems, but also Support Vector Machines, which partially use a neural layer to solve Classification problems.

How to easily tell the difference?

Personally, this is how I easily classify them. First, I identify which tasks I am able to solve with Machine Learning:

  • Regression
  • Classification
  • Clustering
  • Dimensionality Reduction
  • Association

If the algorithm I am using is solving one of these tasks AND does not have any neural layer, then it is Machine Learning. If its architecture has a neural layer (there are extra tasks only solvable by neural networks like Generative Adversarial Network) can be classified as Deep Learning.

AI/ML

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