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Machine Learning vs Deep Learning: What’s the Difference?

Machine Learning (ML) and Deep Learning are two phrases we often hear being tossed around in the realm of artificial intelligence (AI) and new digital technologies. In the media and the public imagination, mostly these two terms are interchangeable, vague, and (arguably) glamorous. Despite the mixed usage, however, they do, in fact, have some key differences. We will be discussing the differences between Machine Learning and Depp Learning in today’s article.

Speaking for myself, the first time I ever heard of the term “Deep Learning” alone in isolation from AI, I thought it was a kind of self-learning strategy. When I was a new content writer, I had one day applied for a “deep learning” writing job thinking it was about some deep education theory. To my surprise, I was accepted immediately! But I had no idea what this “Deep Learning” really was. After doing some research, I realized my mistake, but I decided to delve into it in great detail.

Now, of course, I know that Deep Learning is about artificial intelligence and robot learning, not about humans. That being said, it does have a lot of common elements, especially when we compare human neurology and computing artificial neural networks. Let’s explore what Machine Learning and Deep Learning are and the difference between them.

Difference Between Artificial Intelligence and Machine Learning

Artificial Intelligence is the science of emulating human brain functions with computers and other machines such as robots. It includes self-learning, problem-solving, and so forth.

To simplify the whole issue, everyone can agree that Deep Learning is a special type of Machine Learning and that Machine Learning is a branch of Artificial Intelligence.

Note, however, that this is a simplistic view – in reality, it is much more complicated than that. When we dig in-depth, we will uncover some differences – mostly that those three subjects overlap more than being parts of each other.

However, in most simple cases, Deep Learning is completely still a part of Machine Learning (though personally, I still believe it should also include human and animal learning; this may happen one day when humans and robots combine).

What is Machine Learning?

Machine Learning is a branch of computer science that overlaps with Artificial Intelligence. It aims to mimic the methods of human learning using algorithms and data. It is also an essential element of data science.

In Machine Learning, algorithms can be trained and automatically improved to create some special tasks such as:

  • Future predictions.
  • Classifications.
  • Exploring key insights in data mining.
  • Helping in decision-making for applications and businesses.

Via the use of statistical methods, Machine Learning algorithms establish a learning model to be able to self-work on new tasks that have not been directly programmed for. It is very effective for routines and simple tasks like those that need specific steps to solve some problems, particularly ones traditional algorithms cannot perform. It is helpful for various applied fields such as speech recognition, simple medical tasks, and email filtering.

With the above description, Machine Learning may seem a little boring and not very special at all. When it comes to Deep Learning, however, the real excitement begins. Let us not forget though that Deep Learning is a special type of Machine Learning. So, let’s explore what Deep Learning really is.

Read: Introduction to Machine Learning in Python.

What is Deep Learning?

From its name, we can guess that Deep Learning is more about in-depth learning methods than regular Machine Learning. In fact, there are many factors that differentiate it from traditional Machine Learning, including:

  • How much it needs human supervision.
  • Using algorithms or artificial neural networks that emulate the human brain.
  • Depending on Big Data or statistical data.
  • Utilizing usual computing resources or more powerful ones such as GPUs.

By defining Deep Learning, we can now talk about real AI future applications in many industries such as self-driving cars, medical diagnosis, facial recognition programs, and so on. But to explain deep learning clearly, first, we need to take a quick pass at neural networks, because deep learning also uses methods referred to as deep neural networks.

What are Neural Networks?

Neural Networks are AI techniques and algorithms that take advantage of the nurture neural networks structure. It is a large collection of connected items (artificial neurons) and they are layered upon each other. They are not designed to be exactly as realistic as the brain, but to be more able to model complex problems than Machine Learning.

Some references indicate that the origin of the word “Deep” refers to the hidden layers in the neural network, which can range up to 150 levels. They are distributed mainly on three layers or categories: input layers, hidden (middle) layers, and output layers. Each layer produces its own output. It requires a lot of computing resources and can take a long time to achieve results.

In conventional Machine Learning, we need to manually feed the machine with the properties of the desired output, which may be to recognize a simple picture of some animals, for example. However, Deep Learning uses huge amounts of labeled data alongside neural network architectures to self-learn. This makes them able to take inputs as features at many scales, then merge them in higher feature representations to produce output variables.

Differences Between Machine Learning and Deep Learning

The differences between Machine Learning and Deep Learning are not limited, and they continue to increase as the methodology develops and grows. With that in mind, here are some of the main key differences between ML and Deep Learning:

  • The majority of Deep Learning frameworks were developed by giant software companies such as Google, Facebook, and Microsoft, in part because they have the most powerful infrastructures alongside the huge amounts of data needed to develop deep learning systems.
  • In Deep Learning, there is no need for tagged data for categorizing images (as an example) into different sections in Machine Learning; the raw data is processed in the many layers of neural networks.
  • Machine Learning is more likely to need human intervention and supervision; it is not as standalone as Deep Learning.
  • Deep Learning can also learn from the mistakes that occur, thanks to its hierarchy structure of neural networks, but it needs high-quality data.
    Machine Learning needs less computing resources, data, and time. Deep learning needs more of them due to the level of complexity and mathematical calculations used, especially for GPUs.
  • Both are used for different applications – Machine Learning for less complex tasks (such as predictive programs). Deep Learning is used for real complex applications, such as self-driving cars and drones.

Read: Machine Learning in .Net.

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