Deep learning & machine learning: what’s the difference?

What is the difference between Deep learning and Machine learning? How similar or different are they? How profitable are they for business? Let’s find out!

Machine learning and Deep learning are 2 subsets of artificial intelligence (AI) that have been actively attracting attention for the past two years. If you want a simple explanation of their differences then you are in the right place!

First of all, let’s look at some interesting Deep learning and Machine learning facts and statistics:
The salary of an AI specialist is equal to the cost of a 2017 Roll-Royce Ghost Series II (according to the New York Times);
Is there a chance of losing your job due to the progress of AI? According to a recent PwC report, it is possible. They suggest that by about 2030, 38% of all US jobs could be replaced by artificial intelligence and automation technologies;
The first AI program “The Logic Theorist” was created in 1955 by Newell & Simon (World Information Organization);
Researchers predict that by 2020, 85% of customer interactions will be non-human (Gartner);
The market for artificial intelligence or machine learning will grow to $5.05 billion by 2020 (Motley Fool);
Curious? Now let’s try to understand what is the real difference between Deep learning and Machine learning, and how you can use them for new business opportunities.

Deep learning & machine learning

You must have a basic understanding of Deep learning and Machine learning. For dummies, we present simple definitions:

Machine learning for dummies:

A subset of artificial intelligence concerned with the creation of algorithms that can change themselves without human intervention to produce a desired result – by feeding themselves through structured data.

Deep learning for dummies:

A subset of machine learning where algorithms are created and function similarly to machine learning, but there are many layers of these algorithms, each providing a different interpretation of the data it feeds. Such a network of algorithms is called artificial neural networks. In simple terms, this is reminiscent of the neural connections that are found in the human brain.

For example:


Take a look at the image above. This is a collection of photos of cats and dogs. Now, suppose you want to identify images of dogs and cats separately using Machine learning algorithms and Deep learning neural networks.

Deep learning & Machine learning: when is Machine learning used

To help the ML algorithm classify the images in the collection according to two categories (dogs and cats), it needs to first present those images. But how does the algorithm know which one is which?

The answer to this question is the presence of structured data, as described above in the definition of machine learning for dummies. You simply label images of dogs and cats in order to identify the features of both animals. This data will be enough to train the machine learning algorithm, and then it will continue based on the markings it understands and classify millions of other images of both animals according to the features that it learned earlier.

Deep learning & Machine learning: when is Deep learning used
Deep learning neural networks will take a different approach to solve this problem. The main advantage of Deep learning is that structured/labeled image data is not necessarily needed to classify two animals. In this case, the input data (image data) is sent through different layers of neural networks, with each network hierarchically defining specific features of the images.

This is similar to how our human brain works to solve problems – running queries through various hierarchies of concepts and related questions to find the answer.

After processing the data through various levels of neural networks, the system finds the appropriate identifiers to classify both animals according to their images.

Note. This is just an example to help you understand the differences in how the fundamentals of machine learning and deep learning work. Both Deep learning and Machine learning are not really applicable at the same time in most cases, including this one. You will find out the reason for this later.

Thus, in this example, we have seen that the machine learning algorithm requires labeled/structured data to understand the differences between cat and dog images, learn the classification, and then infer.

On the other hand, the deep learning network was able to classify the images of both animals according to the data processed in the layers of the network. It didn’t require any labeled/structured data as it relied on the different outputs processed by each layer, which were combined to form a single image classification method.

What we have learned:

The main difference between deep learning and machine learning comes from how data is presented to the system. Machine learning algorithms almost always require structured data, while deep learning networks rely on ANN (artificial neural networks) layers.
Machine learning algorithms are designed to “learn” how to act on labeled data and then use it to generate new results with more datasets. However, when the result turns out to be incorrect, it becomes necessary to “learn” them.
Deep learning networks do not require human intervention, as the layered layers in neural networks put data into hierarchies of different concepts that eventually learn from their own mistakes. However, even these can be erroneous if the quality of the data is not good enough.
Data is everything. It is the quality of the data that ultimately determines the quality of the result.


Something that was not in the example, but worth noting:
Because machine learning algorithms require labeled data, they are not suitable for solving complex queries that involve a huge amount of data.
Although in this case we saw the use of Deep learning to solve a minor query, the actual use of deep learning neural networks occurs on a much larger scale. In fact, given the number of layers, hierarchies, and concepts that these networks handle, Deep learning is only suitable for performing complex calculations, not simple ones.
Both of these subsets of AI are somehow related to data, which allows it to represent a certain form of “intelligence”. However, be aware that deep learning requires much more data than a traditional machine learning algorithm. The reason for this is that Deep learning networks can only identify different elements in the layers of neural networks when interacting over a million data points. Machine learning algorithms, on the other hand, are able to learn from pre-programmed, given criteria.

We hope that this example and its explanation made it possible for you to understand the differences between Machine learning and Deep learning. Because this is an explanation for dummies, professional terms were not used here.

Now it’s time to hammer in the final nail. When should you use deep learning or machine learning in your business?