Remember those good and old days when you used to sit infront of your book and learn the topics covered in school? The kind of understanding and satisfaction that you got by studying deeply about a concept can never be replaced by any kind of technology. But with the latest developments in the field of artificial intelligence (AI), nothing seems impossible anymore. 

Be it any kind of learning, we’re always interested to know the basics. They can sound like they mean the same, but it takes just a few minutes to read and understand how distinctly different these two concepts refer to. It is quite essential to understand and know the difference between them. 

You find examples of machine and deep learning everywhere. How does Netflix understand which show you will be watching next? Or how does Facebook understand whose face is there in that photo? How can we even imagine the possibilities of a self-driven car which is very much a reality now? 

The best way to understand and inculcate this unique concept is to understand this simple logic. Deep learning is nothing but machine learning. Deep learning is like a more evolved version of machine learning. It’s a neural network that can be programmed, and this is how you would get accurate decisions without much human help. 

Machine Learning 

Consider this basic definition of machine learning. 

“Certain algorithms that parse data, learn from that particular data and then apply whatever they have learned to make informed decisions” 

Here is an easy example. Consider a music streaming service. You do get automatic suggestions as to which song to consider next. This is where the AI systems programmed with the music service associate your listening preferences to other people’s preferences and gives you a list of entirely different song suggestions that you might actually end up liking! So that is nothing but artificial intelligence or in simple terms, AI. 

Starting from data security firms that track down malware to finance professionals who want alerts for any kind of favourable trades, we see that machine learning gives you all kinds of automatic tasks. The AI algorithms are programmed in such a way that they constantly learn in a way that stimulates as a virtual personal assistant or a VPA. This functions quite well indeed. 

This concept of machine learning involved a lot of complex calculations, including maths and coding that usually serves the same mechanical function that a flashlight or a car or a computer screen does. When we say that a particular thing is capable of machine learning, we usually mean that the thing we are talking about is capable of getting better over time, that is, an artificially made device that learns from its previous mistakes and gets better over time. Consider a flashlight that turned on whenever you said ‘dark’. This means it can recognise different phrases having the word ‘dark’. 

The Difference – Machine learning Vs Deep learning

You can say that deep learning is a subset of machine learning. Machine learning is technically deep learning, and it works in a similar way which is why these terms are often considered the same in meaning. The capabilities are however quite different. The basic machine learning models need to slowly get better at their functioning, whatever that is, but then they still do need guidance. 

In an AI algorithm, if it returns an inaccurate prediction, then the engineer definitely has to step in to make certain adjustments. While with a deep learning model, an algorithm can make out it’s own mistake if a prediction is accurate or if it is not via its own neural network. 

Consider this flashlight example. It can always be programmed to turn on when it recognizes the audible cue of someone uttering the word ‘dark’. As if proceeds to learn, it may eventually turn on with any phrase that has the word. Now if the flashlight had the deep learning model, it can make out that it must turn on when the cues “I can’t see” or when someone says “the light switch will not work”, maybe this can be in tandem with a light sensor. A technique that makes it look like it has its own brain, that’s how the deep learning model is capable of learning through its own ways of computation. 

Consider these key differences between Machine learning and Deep learning:

• Instead of identifying and hand coding the learning systems by humans, a deep learning system can automatically learn these features without much human efforts. For example, a facial recognition program. At first, the program learns and tries to detect and recognize edges and lines of the faces, followed by the more significant features of the face. The data needed is quite huge and with time, the program trains itself, which means that the probability of having correct answers increases. This training in fact, occurs via neural networks which is very similar to the way a human brain functions. This doesn’t even need a human to function with the program.
• As there are many parameters and complicated mathematical functions used, the deep learning systems can take some amount of time to function. Machine learning takes relatively less time, ranging from a few seconds to a few hours. Deep learning systems take about few hours to few weeks!
• As you already know, machine learning and deep learning systems are used for different purposes. They are used in basic machine learning applications which include predictive programs along with email spam identifiers and programs which design the evidence based treatment plans for the medical patients. One really appreciable use of the deep learning systems lay in the self driven cars. Their programs utilize many layers of neural networks to perform functions like find out objects to avoid, to know when to speed up or slow down, to recognize traffic lights and more.
• Because of the complex mathematical calculations and the quantity of data that is processed, deep learning systems need more robust hardwares than machine learning systems. Graphical processing units (GPUs) are used by the deep learning systems. Machine learning systems can work without much computing power and on lower end machines.
• The algorithms which are used in machine learning always tend to parse data in various parts, followed by combination of the parts to come up with a solution or a result. Deep learning systems observe the entire problem in one loop.

Working of Deep Learning 

A model like that of deep learning is designed to constantly analyze the data with a logical structure which can be similar to algorithms called the artificial neural network. This design of an artificially made neural network is inspired from the neural network of a human brain that ultimately proceeds to the process of learning that is way more capable than the standard machine learning models. 

It can be quite difficult to make sure that a deep learning model does not attract conclusions like other examples of AI. It instead needs a lot of training to have the learning processes correct. But when it works as it should be working, functional deep learning is often received as a scientific marvel that a lot of us consider the backbone of real artificial intelligence. One such example is Google’s AlphaGo. 

So if we wind up, the real difference can be summed up under: 

• Machine Learning uses those algorithms to parse the data and learn from that data and therefore make informed decisions that are based on what it has learned. 
• Deep learning is just a subfield of machine learning. Both do fall under the broad category of artificial intelligence, the concept of machine deep learning is what gives energy to the artificial intelligence technology that is very human-like. 
• The algorithms in deep learning are in layers to make an “artificial neural network” that has the capacity to learn and make intelligent decisions on its own.