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In recent years you may have heard about Deep Learning and Machine Learning. But do you know exactly what the difference is between them? They both belong to the group of artificial intelligence but they are not the same thing.
Let’s take a closer look at them here. And also we’ll see how they can be used in the business world.
Deep learning vs machine learning
First, let’s describe the two concepts:
- Machine learning: is a branch of artificial intelligence dedicated to the creation of intelligent algorithms. By this we mean algorithms that are capable of modifying themselves without human intervention thanks to the understanding of data.
- Deep learning: is another branch of artificial intelligence. These are also algorithms capable of self-refining, but in this case there are many layers of algorithms. Each provides a different way of interpreting to interpret the data in a neural network. And this set of interacting layers is called a neural network because it imitates in some ways the functions of the neural networks in the human brain.
As you can see, the difference is complex and subtle. It is best to look at it with an example.
Example clarifying the difference between Machine Learning and Deep Learning
Suppose you have a set of two types of photographs. On the one hand, photographs of trees, and on the other hand, photographs of road bridges.
How does it work with Machine Learning?
The photographs have been specifically labeled with specific tags (structured data). The Machine Learning algorithm is able to detect this data and learn to classify the images according to this pattern. Having learned the pattern, it is able to classify millions of photos as “bridge” or “tree” from the structured data.
How does it work with Deep Learning?
An approach with Deep Learning would be slightly different. With this type of network, the structured data would no longer be needed to distinguish between bridges and trees. An artificial neural network would analyze the image itself (its direct data) and its various layers would define its characteristics to finally distinguish one from another (tree bridges).
The neural network makesperforms queries to its different layers of concept hierarchies and the set of answers allows it to define “what a tree looks like” and “what a bridge looks like”. After an analysis the system knows what the patterns are to classify each type of image.
With this example we see that the difference between Deep Learning and Machine Learning has to do mostly with the way the data is treated in the system. Machine Learning requires structured data and Deep Learning requires layers of artificial neural networks.
Machine Learning algorithms learn to act from the data, and then generate more results from those analyses. If the results are not adequate, they must be reconfigured by humans.
In contrast, a Deep Learning network does not need human intervention. They learn from their own mistakes by filtering out hierarchies of different concepts and finding patterns. Now, if the initial sample is bad or inaccurate, they will have more difficulty doing so. Thus, in Deep Learning the quality of the original data is essential
More particularities that differentiate Deep Learning from Machine Learning
Essentially, Deep Learning works on a much larger scale. Since its function is to identify differences between data sets in order to group the types of images (if we follow the example of the images): it needs much more input data than a Machine Learning algorithm. Machine Learning can learn from criteria that are defined before putting it into operation.
Finally, remember that this is a simple description to understand the fundamental differences between the two elements. But that both Deep Learning and Machine Learning have a complexity that goes far beyond their simple definition.
Let’s finally see how you can take advantage of Machine Learning and Deep Learning in your company.
When to get interested in Deep Learning in my business
We will be interested in Deep Learning if we have a company with thousands or millions of pieces of data that have not been processed or understood. Neural networks will find patterns and draw conclusions useful for business.
These conclusions will allow you to discover flows, processes and opportunities. For example, a car sales website could understand user preferences and discover trends or business opportunities that had not been seen before.
When to be interested in Machine Learning in my business
With Machine Learning you can automate processes, optimizing specific flows and operations of the company from predefined data sets. An airline can use Machine learning to optimize the schedule table of its planes, which have already been previously labeled and classified.
