Machine Learning vs. Deep Learning: What's the difference?

Machine Learning (ML) is the subfield of this big, broad-sweeping category known as Artificial Intelligence. Machine learning gives machines the ability to improve their performance over time, without explicit intervention or help from a human being. Most of the current applications of Machine learning leverage what is known as Supervised Learning.

In practical terms, Deep Learning is a subset of machine learning, which is a subset of Artificial Intelligence; hence, why the terms are used interchangeably. Deep learning is Machine learning but with different capabilities. Deep learning typically has more learning layers than other types of algorithms and these layers are called hidden layers.

Machine learning usually requires some form of guidance, especially if it returns an inaccurate prediction. A person then needs to intervene and make the correct adjustments. But with Deep learning, generally, the algorithms themselves can determine if a prediction is accurate or not.

Let us talk for a moment about some of the differences between Machine learning and Deep learning. While Machine learning models are progressively better at whatever their functions are, the truth is that they still sometimes may need a little bit of hand-holding. If a Machine learning algorithm predicts something incorrectly, someone usually needs to step in and adjust the process. With Deep learning, however, the algorithms themselves are designed such that they can determine on their own whether that prediction is accurate, without any outside help or intervention.

So, in fact, the hierarchy which we have here is that deep learning is a subset of machine learning, and machine learning is a subset of the broader category of Artificial Intelligence.

Here are a few differences between Machine learning and Deep learning that should be highlighted:

Machine Learning 

Deep Learning

How it works
Uses automated
algorithms that learn to
predict future decisions
and model functions
using the data which is
provided.
Interprets features
in data and the
relationships using
neural networks which
pass the data through
several layers of the
algorithm.
Intervention
Algorithms usually
require human
interventions to examine
different variables and
dataset features.
Algorithms require no
human intervention for
data analysis.
Data points
A few hundred to a few thousand.
Can be into the millions.
Output
A numerical value
such as a score or
classification.
Could possibly be
anything?
Hardware
Requires less hardware
capacity than deep
learning.
Requires high- end
hardware capabilities
such as GPUs to perform
at its best.
Feature extraction
Requires features to be identified.
Will look to determine
features from patterns in
data.
Training time
Training time is less.
Training time is more.

 

Let us do a recap:

  • Machine learning uses algorithms to parse data, learn from that data, and then make informed decisions based on what it has learned.
  • Deep learning is a subset of machine learning. It organizes algorithms in layers to create an artificial neural network that can learn and make intelligent decisions without any outside intervention and by finding patterns in the data provided.
  • Deep learning uses a massive amount of data to learn. With the phenomenal increase in the amount of data we collect, in the very near future, we hope that deep learning will be able to provide new opportunities and innovations.

Hope this was helpful.

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