These are the 7 Differences between Machine Learning and Deep Learning

There are some significant differences between machine learning and deep learning. Machine learning and deep learning are still both part of artificial intelligence (AI), but both have their functions that are discernible from each other.

Examples of implementing machine learning and deep learning are all around us. These two things are what drives self-driving cars to work, how YouTube, Spotify, and Netflix provide music and watch recommendations, and how the Face ID feature can recognize your face.

Because of their usefulness, machine learning and deep learning are often considered the same thing when they are not. Through this article, you will be able to find out what is the difference between machine learning and deep learning. However, before discussing this, let’s look at an in-depth explanation of the two terms first.

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Machine learning vs Deep Learning

In simple terms, Machine learning is the application of Artificial Intelligence (AI) that makes algorithms parse data, learn from that data, and then apply what has been learned to make the right decisions. Meanwhile, deep learning is one of the fields in Machine learning that builds algorithms in layers to create “artificial neural networks” that can learn and make intelligent decisions on their own.

The most common example of applying Machine learning is on music streaming services such as Spotify. For Spotify to make decisions about which new songs or artists to recommend to users, Machine learning algorithms will associate the user’s preferences with other users who have similar musical tastes. So, the Machine learning algorithm will decipher the data (user preferences), learn from the data, and make the right decisions (provide recommendations).

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On the other hand, a concrete example of applying deep learning is Google AlphaGo, which is a computer program with a neural network that learns to play the game ‘Go’. By playing against professional Go players, AlphaGo’s deep learning model will learn how professional Go players play so that they can imitate everything that professional Go players do.

Differences between Machine learning and Deep Learning

Machine learning and deep learning are two different things from how they work to how they are implemented. To be clear, let’s look at some of the differences between Machine learning and deep learning that you must know below!

1. Number of Data

The most striking difference between machine learning and deep learning is the amount of data required to process. Machine learning Algorithms make it possible to process and process data in various amounts, both large and small. On the other hand, deep learning algorithms tend to require only large amounts of data to process and process.

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2. Duration of Execution

The next difference between Machine learning and deep learning is the length of execution time. Algorithms from Machine learning only take minutes to hours to execute data. Meanwhile, deep learning algorithms can take anywhere from days to weeks to execute data. This is because deep learning is mostly used to solve very large amounts of data so it takes a significantly longer time.

Meanwhile, deep learning algorithms can take anywhere from days to weeks to execute data. This is because deep learning is more widely used to solve very large amounts of data.

3. Hardware

Deep learning algorithms will work much better if they use machines or hardware with large capacities that are capable of processing and processing large amounts of data. Usually, the hardware used for deep learning must have a good GPU (Graphics Processing Unit) quality.

Meanwhile, you can use hardware of mediocre quality for Machine learning algorithms. The reason is, Machine learning is more often used to process and process data in small to moderate amounts and not as big as data processed by deep learning.

4. Approach

The approaches used to solve the problems of the two learning methods are very different. To solve a given problem, the traditional Machine learning algorithm will first break the inner problem into several parts, and after completing each part, a new final result is created.

The problem-solving approach used by deep learning algorithms is different from traditional Machine learning algorithms. Deep learning algorithms will see the entire problem given in one big picture. This means that deep learning will solve the whole problem, from start to finish in real-time.

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5. Feature Engineering

Machine learning Algorithm requires feature extraction by experts to proceed to a more advanced process. Meanwhile, deep learning algorithms will try to do it themselves by studying the existing data. Deep learning is a machine learning model that is always being refined regularly.

6. Result Interpretation

In Machine learning, interpretation of the results for a given problem is relatively easy. When working with Machine learning, you can easily interpret the results, such as why these results occur and how the process occurs.

Meanwhile, interpretation of the results for a given problem in deep learning is very difficult. When we work with deep learning, you may indeed get better results from Machine learning, but it is very difficult to understand why these results occur.

7. Application

Machine learning Algorithms are more suitable for solving simple and less complex problems. Meanwhile, deep learning algorithms will be more suitable for solving complex problems.

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Those are the differences between Machine learning and deep learning that you need to understand so that there are no misunderstandings. Both are indeed equally able to facilitate various kinds of business, especially data processing. If you want to use technology that can make your job easier, try using the OCR application from AdIns. Want to know more about our OCR application? Contact AdIns now for more information!