There is a bunch of people that don’t really know if there’s any difference between in such hyped terms like machine learning and deep learning and how these two are related to each other. Before we go any further, it’s worth mentioning that even though AI, ML, and deep learning often times are used instead of one another, especially by some businessmen, marketers or startuppers, but these terms are not all the same! So, please, stop muddle them!
Scratching the surface: terms and definitions
To start with, artificial intelligence is a much broader term that encompasses machine learning and its smaller subset of deep learning. Do you see where we’re going with this? If you have a computer working on a certain task with the help of algorithms, you can’t call it artificial intelligence. However, you can call it machine learning, but let’s not rush things and dive a bit deeper.
So, machine learning is the ability of a machine to learn from its experience going through a number of tasks. Imagine you have a data set that you can feed to the machine in order to make it learn from and recognize its objects. Here’s the most important part: if you try and use an algorithm to foresee the result of an event, you cannot consider this process to be machine learning. Instead, when you use the result to refine future predictions and make them more accurate, this is when machine learning development takes place.
But there’s another level of “intelligence” in machine training, too. Deep learning is considered to be just another way of implementing machine learning or its daughter category. Think of it this way: deep learning is an upgraded version of machine learning. Why? Because its algorithms are inspired by the performance of the human brain and aim at mirroring its activity. Similarly to a human being using their brains to spot patterns and cluster different kinds of data, deep learning tools can be educated to perform identical tasks for the machines.
Going deeper into solids: machine and deep learning compared
If you compare the day-to-day of a human brain that usually decoded the information coming from the outer setting, you’d see that that’s what science in deep learning field is trying to echo. Similarly to the brain, machines are on a mission to decompose the information and assign classes.
What it means is this: if we perceive some new info, the natural function of our brain would be to try to give it an explanation based on past experiences (our archives). This very notion is taken and put at the core of deep learning concept powered by algorithms. But what is the ideal setting for a planted seed (algorithm) to root?
The answer is artificial neural networks or ANNs for short. Having a layered structure, ANNs channel the data through the input, hidden (one or many) and output levels.
Let us explain. Usually, a neural network is first educated through a series of big data. The educational process involves the input and telling ANN about the desired outcome. Imagine what it would be like to teach the network to recognize animals. Then, the initial training would be about feeding it photos of animals with associated names as well as non-animals, so the network can improve over time.
So it all adds up to this: these algorithms try to copy our brain activity when it comes to drawing conclusions. The latter process is about the information that is inferred, implied or assumed.
Make no mistake about it. That’s the job of deep learning that automatically spots the parameters for clustering the information it’s been fed, on the one hand. On the other hand, machine learning needs such parameters to be introduced along with the training data set.
But one thing’s for sure: usually, DL requires big data to produce high-quality accurate predictions. And oftentimes scientists struggle to get training sets that would help to deal with industry-specific tasks: big data tailored to certain business domains e.g. medical or space could cost an arm and a leg.
Real life use cases
First off, the majority of reputed payment (e.g. PayPal) solutions utilize the power of machine learning and algorithms to deal with financial crimes. To top it off, your favorite TV series suggestion engine (e.g. Netflix) is another use case of machine learning that powers intelligent entertainment. After all, Pinterest became even cooler with its relevant content following the acquisition of machine learning firm Kosei. Lyft and Uber are transportation pioneers that built up an empire by employing algorithms to optimize the operations and overall performance.
The use cases with both machine and deep learning in focus are countless. Speaking area wise, Forbes compiled its list of practical scenarios of successful harnessing the vast potential of deep learning, too with news aggregation and customer experience instruments being among others. In addition to that, think of all the ads served to you using the cookies and your search history: it’s very unlikely you’ll see content that has absolutely nothing to do with you. Why? It is mainly because machine learning aims at helping marketers with better targeting and customization.
Let’s see if we’ve been on the same wavelength throughout the article. The simple truth is artificial intelligence is a much larger topic than machine learning and the latter is a parent category of deep learning. So you have a Russian doll type of relationship between the concepts.
With artificial intelligence being the largest and the most senior “matryoshka”, machine learning is a subcategory of AI that concentrates on the ability of computers to process data sets and educate themselves altering algorithms as they progress with the big data being processed.
Typical machine learning models improve with time. However, they still require a helping hand of a craftsman — an engineer to guide them if an error occurs. By contrast, with deep learning, the algorithms are smart enough to identify if the prediction is correct or not without any assistance.
Training machine to resemble a human with regard to data processing and classification is achieved by using ANNs: special algorithms created after human brain that is able to identify similarities and cluster the info.