Original article was published by Harsh Patel on Becoming Human: Artificial Intelligence Magazine
“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:-
- 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.
- The Term that puts Machine Learning behind Deep Learning for Complex Dataset is “Curse of Dimensionality”.
- 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.
Importance of Feature Selection Preference.
→ In Machine Learning Technique the first stage of creating any model is Data Preprocessing in which there is a term Feature Selection, which impacts the model a lot.
→ But in Deep Learning Technique Feature Selection is not that important as it is in Machine Learning Technique. In Deep Learning with Forward and Backward Propagation weights get automatically updated and it automatically assigns preference to the Feature which has a higher impact on the Model.
Impact of Resources.
“The analogy to deep learning is that the rocket engine is the deep learning model and the fuel is the huge amounts of data we can feed to these algorithms.”
→ Machine Learning is more Popular than Deep Learning Technique because of its Usability. for Deep Learning we require a large dataset with higher GPUs because steps taken in a Deep Learning Technique are more than as it is in Machine Learning so for better Performance of Model, High Specification of GPUs are required. but if we consider a Machine Learning Technique it will work better than a Deep Learning Technique when we have Few Resouces(Less Complex Dataset and Low Specification of GPU).
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Last but not the least, we have interpretability as a factor for comparison of machine learning and deep learning. This factor is the main reason deep learning is still thought 10 times before its use in industry.
Let’s take an example. Suppose we use deep learning to give automated scoring to essays. The performance it gives in scoring is quite excellent and is near-human performance. But there’s is an issue. It does not reveal why it has given that score. Indeed mathematically you can find out which nodes of a deep neural network were activated, but we don’t know what their neurons were supposed to model and what these layers of neurons were doing collectively. So we fail to interpret the results.
On the other hand, machine learning algorithms like decision trees give us crisp rules as to why it chose what it chose, so it is particularly easy to interpret the reasoning behind it. Therefore, algorithms like decision trees and linear/logistic regression are primarily used in the industry for interpretability.
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Should we use Machine Learning or Deep Learning? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.