Machine Learning vs. Deep Learning: 5 Key Distinctions

17 November 2020

The notions of machine learning (denoted ML) and deep learning (DL) are often (wrongly) used interchangeably. The two aren’t the same. While both are concepts within the Artificial Intelligence (AI) spectrum, they are significantly different in both definitions and applications.

Artificial intelligence AI research of robot and cyborg development for future of people living. Digital data mining and machine learning technology design for computer brain communication.

Understanding the Origins of ML and DL

Before we get to the details of each technology, let’s try to understand what AI entails and where ML & DL originate.

Artificial Intelligence is the theory and development of computer systems that can perform tasks requiring human intelligence. These typically include visual perception, speech recognition, decision-making, and translating languages.

AI systems are called “smart” because they can perform these tasks almost as well as humans. Many AI systems feature built-in software and hardware to analyze data and extract the insight necessary to execute these tasks.

However, not all AI solutions are smart enough to learn on their own – which is where machine learning and deep learning come in handy. These two technologies help AI machines learn from the available data.


Machine learning

The bigger technology (in terms of scope) involves computer systems programmed to learn from data that’s input without being continually reprogrammed. Put differently; machine learning tools continuously improve their performance on the job – such as playing a game repeatedly – without much human intervention.

Arthur Samuel reported the first instance of machine learning in 1952. He used a relatively simple search tree to develop an IBM computer that could continually improve at checkers.

Deep learning 

Deep learning is essentially a subset of machine learning. Indeed, a few experts consider it the next frontier in machine learning – the cutting edge of the cutting edge. It is a concept deep within machine learning that uses artificial neural networks designed to imitate the way humans think and learn.

So, whereas machine learning involves more straightforward concepts such as predictive models, deep learning comprises much more complex mathematical calculations.

Key Distinctions

The following are five other clear distinctions between the two concepts;

  • Human intervention

The first main difference between machine learning and deep learning is in the level of human intervention. Machine learning systems require a human to identify and hand-code the applied features based on the data type, e.g., orientation, pixel value, etc. Deep learning doesn’t require similar intervention. Instead, the deep learning system tries to learn on its own.

An excellent example is a facial recognition program. Machine learning allows facial recognition software to detect and recognize edges and lines of faces by continually feeding new information and data. 

Meanwhile, deep learning systems decode on their own by training to identify the correct face through neural networks. Very similar to how the human brain functions – without further need for a human to recode the system.

  • Hardware differences

Both machine learning and deep learning solutions require super computing systems. Particularly because the systems need to process huge amounts of data. Even more powerful hardware is vital where the machine transmits and relays results in real-time.

However, deep learning systems require more powerful and efficient hardware. Why? Because, as we’ve mentioned, they perform complex mathematical calculations. 

Secondly, deep learning machines also process more data per unit time, making the need for processing power even greater. A common type of hardware used in deep learning is graphical processing units (GPUs). Machine learning solutions don’t require as much power.

  • Processing time 

Another critical distinction between deep learning and machine learning is processing time, or the time it takes to get results from the system. In both cases, enormous effort is required to train the system to deliver the right results. The face recognition software, for instance, has taken years of training to get where we are – and it’s still not perfect.

Of the two technologies, however, deep learning takes more time. The first reason is the massive amount of data and more complicated mathematical calculations. 

However, another bigger reason is that deep learning machines are required to pick up on their own. Whereas the machine learning solution counts on occasional human intervention to overcome some challenges, the deep learning solution must decipher answers independently. This can take significantly more time.

  • Processing approach 

The algorithms used in machine learning tend to parse data in parts. These parts are then combined to form the result of the solution. Deep learning systems don’t use the same approach. Instead, they look at the entire problem and attempt to resolve it in a full swoop.

An excellent way to explain this is to consider how machine learning and deep learning systems would identify license plates in a parking lot. Machine learning solutions would perform the task in two steps – detecting the plates, then identifying each plate number. However, a deep learning tool would return both the plate’s identity and location in one go.

  • Applications  

Finally, machine learning and deep learning are used in different applications. Basic machine learning applications include predictive programs, such as forecasting where the next hurricane will hit or the prices in the stock market. 

Machine learning is also exceptional at detecting email spam and designing evidence-based treatment plans for medical patients.

With deep learning, one of the most popular applications is what you see on Netflix. After using the Netflix platform for an extended period, the platform gets to a point where it can near-correctly recommend what you’d like to watch next. 

Another well-publicized application of deep learning is self-driving cars. These cars leverage complex neural programs to recognize the road, read traffic lights, determine objects to avoid, and know when to slow down.

The Two Could Change the World

What’s most important about machine learning and deep learning, though, isn’t the many distinctions but how, together, they could power the future.

Gartner predicts significantly increased machine learning applications over the next two years. It’s projecting 75% new end-user solutions in areas including robots, cognitive services, and product personalization. 

Meanwhile, deep learning is expected to surpass the human brain in processing data, especially in real-time.

If you’re currently working on a machine learning or deep learning project and need an expert’s opinion or help, don’t hesitate to call NIX Solutions.