Deep learning vs. machine learning is still the most confusing term for many people. In the AI world, these terms are used interchangeably by non-techie persons, but that’s not true.
To understand how deep learning differs from machine learning, you need first to understand artificial intelligence. And let me tell you that these terms are straightforward to understand.
The artificial intelligence term was first coined by John McCarthy in 1956. In simple terms, Artificial intelligence is the development of a computer system that can perform a task that usually needs human intervention or intelligence.
So, the terms deep learning and machine learning arose from Artificial intelligence. In the most straightforward words, deep learning is the subset of machine learning, which, in turn, is a subset of artificial intelligence.
Now, to understand the actual difference between machine learning and deep learning, we need to dig a bit deeper.
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Machine Learning vs. Deep Learning: Quick Overview
Without discussing the terms machine learning and deep learning in heavy technical words, I would choose the most straightforward way first.
The easiest way to understand the difference between machine learning and deep learning is to know that deep understanding is machine learning.
Okay, I know that’s too short for understanding these terms. To explain in more detail, I would say that deep learning is the evolution of machine learning, which uses the artificial neural network (ANN) to make more accurate decisions without any intervention of humans.
We will see more differences between machine and deep learning in this article based on different aspects, so stay tuned.
Let’s take this discussion one more step further better to understand Machine learning and deep learning in more depth.
Elaborated difference between deep learning vs. machine learning.
What is Machine Learning?
In the most straightforward words, machine learning is a machine to learn’. As a definition to say, “Machine learning is the set of algorithms that parse the data and helps the machine to learn things from the experience and mistakes to make an instructed decisions.”
In general, machine learning works on the algorithms that parse the structured data for getting the informed outputs. These learning processes may or may not be supervised depending on the data being feed to the algorithms, and it does not need to code explicitly whenever the input changes.
Take the example of Amazon, an E-Commerce giant, to better understand machine learning. Amazon’s Machine learning algorithms use the user’s data to make multiple decisions on their own. Based on the past shopping experience, these algorithms decide when and what offers to send to the particular users and what products to be listed on the user account’s home page. These algorithms also help Amazon calculate how much stock it needs to manage and finalize the product pricing for different customers as per the geological area.
Machine learning was the new boom in the computer industry, and still, it is used in large companies for product improvement, automating tasks, and hunt down malware.
Machine learning includes lots of complex data, coding, and maths to improve the decision capability without losing the progression gradually.
Now, it’s a time for understanding deep learning, which is relatively more interesting than what it actually looks like. If you have got a basic idea about machine learning, then it would help you to understand the difference between machine learning and deep learning easily.
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What is deep learning?
As said earlier, the term deep learning arose from machine learning, same as the term machine learning arose from artificial intelligence.
Talking about the definition, one can confidently say that “Deep learning is the evolution of machine learning algorithms which is sophisticated and mathematically very complex”.
As the IT industry grew drastically after the internet became publicly available, the users’ data also increased. Handling this data and sorting it out for product development or customer benefits also became one of the most critical tasks. And that’s where the deep learning term was coined, and Igor Aizenberg first mentioned it in 2000.
In simple terms, to explain deep learning, “An algorithm which is inspired by the structure and function of the human brain”. It works similarly to how the human brain draws the conclusion. As I stated earlier, deep learning algorithms use the layered structure of algorithms called Artificial neural networks inspired by the human brain.
Deep learning generally deals with a large amount of data, unlike machine learning, which is not suitable for heavy data inputs.
It may not be evident to differentiate deep learning from machine learning but understand that deep learning takes a lot of time to learn things for drawing accurate decisions.
For an easy understanding, let us take the example of the world-famous car company Tesla. In the tesla cars, the autopilot mode uses lots of different deep learning algorithms to identify the objects like a STOP sign, nearby vehicles, vehicles, speed breakers, and dividers. Consider an example of the STOP sign to break down the working of deep learning here.
The Artificial neural network will first identify the STOP sign’s comparable properties, which are also called features. These features can be an edge, colors, shapes, and size. In the machine learning algorithm, we would need to feed the multiple images of the STOP sign to the algorithm, and every time there is a change in the STOP sign, a software engineer needs to select the relevant features.
Unlike this, in deep learning, an artificial neural network is capable of identifying the change in features like edges, colors, and size from the shape of the object. Deep learning algorithms learn from the own errors, change in objects or features when fed up with the training data.
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The difference between Machine learning and deep learning
You have read this so far and might have got an idea in your mind what actually makes deep learning different from machine learning.
Practically, deep learning vs. machine learning term should not be asked in this way. Because deep learning is actually machine learning, instead of understanding the difference, it is good to think about what makes deep learning special in machine learning.
Let us look at the significant difference between deep learning and machine learning based on the following points.
Training: As machine learning is based on a simple structure compared to deep learning, it takes slightly low time to train or execute the particular model or a system. Though, the time varies from few days to a few weeks. On the other hand, as the deep learning algorithms are based on complex and intertwined neural structures, it takes more time to train the model, and the time varies from a couple of weeks to a month.
Human Intervention: Deep learning algorithms require very little human intervention as the neural networks can learn and make decisions more accurately on their own. To explain in short, with an example, deep learning algorithms can identify the human faces more accurately from the relevant features like nose, ears, and eyes without feeding the extra image data for each different look.
Data Handling: Though the deep learning term is coined from machine learning, it is more advanced in nature. That is why deep understanding is used to handle and sort a large amount of data instead of machine learning. Though deep learning can take a large amount of data, deep learning and machine learning have their own advantages.
Execution Time: Execution is precisely opposite to the training factor where deep learning algorithm takes low time to make a decision than the machine learning algorithms. Hence, deep learning is more preferable to machine learning. The data grows exponentially, and one can not say what type of information is being generated or fed, whether it is structured or unstructured.
I hope that you have got an idea about the difference between deep learning and machine learning. The upcoming years are full of competition where you would see many jobs will be killed by AI, machine learning, and deep learning. Alternatively, it would create many jobs as programmers and developers in the respective field. So, it is the best time to learn the hot cutting-edge skills right now. Stay tuned with the — for such great informational content, and Have a great day ahead!