There is often a lot of confusion around the differences between machine learning and deep learning. Both are classed as techniques to enable artificial intellgence or AI. But what is AI?
AI is the ability to create a program or computer system that can fool a human into thinking it is another human. There is a simple test for this called the Turing Test, developed by Alan Turing. Turing is a famous computer scientist who is potrayed in the film 'The Imitation Game'. He was the UK's secret weapon in the 2nd World War.
The test is very simple. There are 3 actors. A computer, person B and our interrogator C. Each actor is placed a separate room. If the interrogator is unable to determine which actor is the computer, then the computer is determined to be intelligence, albeit it's artificial. Here is a simple diagram outlining this concept:
Coming back to the machine learning and deep learning techniques, let's define those in turn. We can use either or both techniques to fool our interrogator into thinking our computer is intelligent.
Deep Learning - is the process of applying neural network theory to help a computer learn. Neural network theory strives to mimic our brain function. Our brains are made of neurons and pathways, known as neural networks. With deep learning we setup virtual neurons and virtual gateways in our system and use similar biological rules to allow the network to start learning. In order to understand neural networks in more detail, you'll need to cover some psychobiology theory that outlines how the brain works. Here is a simple video on how neurons work:
The diagram below shows some of the many possible neural networks that you can choose from:
Machine Learning - is the process of applying mathematical models to help a computer learn. It does not attempt to mimic the brain in terms of structure, but instead provides a process for allowing a computer to learn via mathematical techniques. There are 100's of mathematical methods to enable machine learning. Some examples include: random forest, regression and dedcision trees. Here is a great example of a decision tree:
And finally to put things in context, we can see how AI, Machine Learning and Deep Learning has evolved over time in the diagram below. This is also a differentiator between machine learning and deep learning. As you can see deep learning is a newer technique, inspired by human biology, whereas machine learning is an older technique, inspired by various mathematicians:
Check out the Nvidia blog that accompanies the picture...BTW....they provide the deep learning framework for Tesla cars....
As you can see, the biggest challenge that a data scientist has to content with, is which deep learning or machine learning technique to use. That's of course once we have a clearly defined business requirement and/or outcome we're looking for and we've probably spend days or months trying to obtain clean data. Oh the joys of data science.....
Paul Colmer is a digital coach for ALC training and consulting, with a real passion for learning and applying disruptive technologies. Paul has responsibility for building and delivering ALC's digital architecture strategy and the development and execution of a number of cloud courses, including Cloud Security (CCSP), Amazon AWS, DevOps, Microsoft Azure and Office 365.