Raise your hand if you have ever been confused when comparing machine learning vs deep learning. Well, the good thing is, you aren’t the only one. Artificial intelligence (AI) is a very broad concept and the latest technological buzzword. Companies around the world now want to incorporate AI in their business processes in some way or the other just to get ahead of the curve. While understanding the basics of artificial intelligence can seem rather overwhelming because it is such a broad concept, you can start with the two main subsets of AI — machine learning and artificial intelligence. These two terms are often used interchangeably, but they are actually very different from each other.
In this article, we will be covering machine learning vs deep learning, and everything in between.
Machine Learning vs Deep Learning: Understanding the Difference
Before we go into the detailed comparison, let’s discuss what these terms really mean:
What is Machine Learning?
Machine learning is a subset of artificial intelligence which includes algorithms that can learn from data and improve on their own to produce the desired output. An excellent example of machine learning is an on-demand music streaming service like Spotify that can automatically create curated playlists for users based on the songs they like. The algorithm compares the user’s music preferences with other users who listen to similar songs in order to make accurate recommendations.
What is Deep Learning?
A subset of machine learning, deep learning models are mainly designed to continuously analyze the data with a logical structure that is similar to how humans would draw conclusions. While deep learning is technically machine learning and it functions in a similar way, its capabilities are very different.
While machine learning algorithms are linear in nature, deep learning algorithms have different levels of hierarchy (also known as artificial neural networks) with increasing complexity.
Every algorithm present in the hierarchy applies some non-linear transformation to the input data and uses what it learns from the data to create a statistical model as the output. Then the iterations continue until a certain acceptable accuracy level is reached. The wide number of processing layers present is the reason why these algorithms are called ‘deep’ learning.
The difference between Machine Learning and Deep Learning
In machine learning algorithms, if the prediction made by the machine is inaccurate, then an engineer can step in to make the required changes. But with deep learning, the algorithm has the ability to determine on its own whether the prediction is correct or not with the help of its neural network.
For instance, let’s assume we have a collection of cat and dog pictures and we want to identify the images of cats and dogs separately with the help of machine learning and deep learning. With machine learning, the easiest way is to structure data. You can label the pictures of cats and dogs in a way that defines specific features of the animals. Machine learning algorithms can then use these labels to classify pictures based on the unique features of the two animals.
But deep learning algorithms would handle things a little differently. After all, the biggest advantage of deep learning networks is that they don’t require labeled or structured data to make predictions. The artificial neural networks can directly send the input through the different layers in a hierarchical way to define the specific features of the given images and identify both the animals accurately
Here are some of the biggest differences between machine learning and deep learning:
1- Data dependencies
When it comes to machine learning vs deep learning, the biggest difference between the two algorithms is how their performance changes as the scale of data steadily increase.
When the data is still small in size, machine learning algorithms perform much better than deep learning because the former already has handcrafted rules, while the latter requires a large amount of data to understand it perfectly and generate output accurately.
2- Hardware dependencies
As deep learning algorithms are rather advanced, they need to depend on high-end machines to run successfully. On the contrary, machine learning algorithms can even run easily on low-end machines. That is because deep learning algorithms perform large iterative calculations that require utilizing the GPU of the machine.
When it comes to algorithms and decisions made by machines, it is not enough to know the final prediction. We also need to know ‘why’ that particular prediction was made by a machine and that phenomenon is known as interpretability. The interpretability of machine learning is much higher than deep learning which is why the former is still preferred over the latter.
To understand interpretability, let’s take an example where a deep learning algorithm is supposed to automatically score essays submitted by students. The grades that the algorithm gives is almost accurate, but there is one very big issue — The algorithm doesn’t clearly reveal why it has given all the scores.
Of course, you can mathematically find out which parts of the deep neural network were activated, but it’s almost impossible to figure out why those parts were activated and how they gave a particular prediction.
On the other hand, since machine learning algorithms work like decision trees, they have clear rules as to why they choose a particular prediction which makes it easier to interpret the reasoning.
As more and more companies adopt machine learning and deep learning, there is an increased need for experts in these verticals.
Springboard offers targeted courses for working professionals that are hoping to pivot in their careers. The courses include all the crucial in-demand skills and come with 1:1 mentoring-led by the experts in the industry.
There is also a dedicated machine learning course available on Springboard that can make you an expert machine learning engineer in just 6 months and get you a guaranteed job. Take a look to request the syllabus and apply now.