Deep Learning vs Machine Learning

Words like Machine Learning, Deep Learning, and Artificial Intelligence are often used interchangeably by laypersons. However, for a specializing IT professional building a career in the domain, understanding each of these terms, what they signify, and how they differ is critical. 

According to the official definition, artificial intelligence refers to “the theory and development of computer systems to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” However, not all AI systems learn without help. These AI-enabled systems vary on several broad and intricate factors; this is where Deep Learning and Machine Learning play a role. 

Machine Learning vs. Deep Learning:

When discussing Machine Learning vs. Deep Learning, it is critical to understand what these represent. DL and ML are both subsets of Artificial Intelligence, serving unique and specialized functions. 

Let’s take a look at both in more detail.

What is Machine Learning?

Machine learning refers to machines that learn from data fed to them as a generic term. The domain of machine learning encompasses the integration of computer science with statistics, where algorithms enable machines to carry out tasks without the need for explicit programming or manual intervention. Machine Learning systems recognize data and behavioral patterns and make any predictions and changes when new data comes in.

Typically, the learning process that Machine Learning algorithms follow can be unsupervised or supervised, based on the data feeding the algorithms. Traditional ML algorithms are often simple, like linear regression. For example, consider a situation where a user wants to predict their income based on their higher education and experience. The first step would be defining a specific function, such as 

‘Income = y + x * number of years of study’

Once you have this function, give the algorithm a training data set, such as a data table showing x years and the adjoining income rise or drop. Once this is done, let the algorithm draw a line and plot the data. Finally, give your algorithm test data that you can use to test and teach your machine and algorithm.

So, to sum up, what we understand about Machine Learning is:

  • Machine Learning combines statistics and computer science, allowing computers to learn without explicit programming.
  • Supervised and unsupervised learning are two broad aspects of machine learning problem-solving. 
  • Machine Learning algorithms can be as simple as OLS regressions.

Must Read: Popular Machine Learning Certifications To Get This Year

What is Deep Learning?

Deep Learning algorithms are best understood as both sophisticated and mathematically complex forms of ML algorithms. The Deep Learning domain has recently been under the spotlight, although not undeservedly. This technology has provided results that have been deemed impossible in the past.

Deep Learning as a domain covers algorithms used for analyzing data with logic structures similar to the human brain drawing conclusions. This can take place in both unsupervised as well as supervised learning conditions. A deep learning application uses a layered algorithmic structure known as an ANN (Artificial Neural Network) to achieve this result. An ANN is created by taking inspiration from the natural neural networks in the human brain, which leads to far more capable learning processes than standard Machine Learning models use. 

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5 Major Differences in Machine Learning vs. Deep Learning:

1. Human intervention:

Machine Learning systems need human support to manually identify and code applied features depending on the type of data (pixel value, orientation, shape, etc.). Meanwhile, Deep Learning systems aim at identifying these features without too much human intervention. 

Consider the example of facial recognition software. First, the program learns how to recognize and detect lines and edges on a face, then the more detailed areas of the face, and finally, the unique traits of the face. The data that goes into such a software solution is significantly large. As time passes and the program starts training itself, the accuracy of facial recognition will increase. This training takes place using neural networks, similar to how humans create facial recognition patterns in their brains, without requiring any program recoding. 

2. Hardware requirements:

The volume of data being processed and the complex nature of the calculations involved in Deep Learning algorithms are quite high. Therefore, these systems need much stronger hardware than a Machine Learning system. Graphical Processing Units or GPUs are one hardware type that Deep Learning systems use. A Machine Learning system can run programs using lower-end machines without too much computing power. 

3. Time needed:

Deep Learning systems use large volumes of data sets, as mentioned before. With so many complex parameters and mathematical formulae required, training Deep Learning systems takes a long time to train, up to several weeks. Meanwhile, a Machine Learning system gets trained anywhere between a few seconds to hours.

4. Approach:

Machine Learning algorithms generally parse data in bits. These bits then combine to form the desired result or solution. A Deep Learning system takes in complete scenarios or problems in one go. Consider an example where you want a solution that identifies specific objects within an image, such as car license plates, headlights on in parking lots, traffic light colors, etc. This takes up two steps in Machine Learning - start with object detection and then object recognition. In a Deep Learning system, input the image into the system. With time and training, the program will return the specific objects as well as their location within the image in one go. 

5. Applications in the real world:

With the specific differences given above, the differences in their definitions, and the way they work, by now, you probably understand that Machine Learning and Deep Learning are far from being synonyms. It is also clear that both these branches of AI have very different applications. 

Some areas where Machine Learning systems are required are predictive domains (stock market forecasting or weather and meteorological predictions), identifying spam in the mail, and medical solutions designing data-based treatment schedules for patients. Music streaming services, OTT platforms, facial recognition, and self-driving cars are all Deep Learning systems and technology applications. Such programs use multi-layered neural networks to find which object to avoid, identify the color of the traffic signal and ensure appropriate driving speed and speeding up and slowing up conditions.

Machine Learning and Deep Learning Trends in the Market:

Machine Learning and Deep Learning have a sea of endless possibilities in the future, with new opportunities arising every year. The exponentially rising domain of robotics has created several new applications, not only in the manufacturing industry but in several independent domains, that can improve individual and corporate lives in several ways. The healthcare industry is currently being revolutionized, with doctors getting help with several complex tasks like predicting or detecting cancer early to save several lives. In the finance industry, Machine Learning and Deep Learning are set to allow businesses and people to invest better, save more and designate resources efficiently. 

These are just a few of the top applications of ML and Deep Learning. Several ideas are in the initial stages only, while others are still waiting to come alive. 

Also Read: Data Science vs Machine Learning and Artificial Intelligence

Future of Machine Learning and Deep Learning:

The data boom is advancing exponentially, as we’ve observed in the past decade. Both Machine Learning and Deep Learning are predicted to impact our day-to-day lives for decades to come and transform every industry with their capabilities. High-risk tasks and operations such as interplanetary travel or working in unfavorable environments could soon be changed completely with the involvement of machines. Meanwhile, laypeople have already started turning to AI for rich and innovative entertainment experiences that were once only possible in science fiction. 

To understand how Machine Learning and Deep Learning work, enroll in an Artificial Intelligence certification course with Koenig. Here you will get a strategic study plan based on the domain you choose, expert mentorship with industry veterans, updated study resources and practice tests, and flexible learning hours. Give your career a boost and enroll in a training course on Koenig today.

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Armin Vans
Archer Charles has top education industry knowledge with 4 years of experience. Being a passionate blogger also does blogging on the technology niche.



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