Should we use Machine Learning or Deep Learning?

Should we use Machine Learning or Deep Learning?

To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own.

“Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.”

This is a very important question for many newcomers in the field of Machine Learning/Deep Learning/Data Science.

So, Today we are Gonna find this out:-

  1. Generally, we can say for less number of features we should use Machine Learning model, it's faster than a Deep Learning Technique and has better accuracy for Less Features.
  2. The Term that puts Machine Learning behind Deep Learning for Complex Dataset is “Curse of Dimensionality”.
  3. Curse of Dimensionality:-The curse of dimensionality basically means that the error increases with the increase in the number of features. It refers to the fact that algorithms are harder to design in high dimensions and often have a running time exponential in the dimensions.

How Deep Learning is better in Terms of Curse of Dimensionality?

→ Look as we all know Deep Learning contains various layers and neurons which carry weights and with every epoch, we update the weights, and due to this Complex number of Hyperparameter Tuning, Deep Learning Techniques Decreases the Curse of Dimensionality.

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