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| Photo: Naveen Joshi |
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| Photo: BBN Times |
Over the past few years, the growth in technologies like AI, big data, and blockchain has offered incredible benefits to both, the industries and the end users. As a result, AI is gaining a lot of attention due to its ability to create machines that can behave intelligently and smartly, like humans. However, AI is broadly classified into two major concepts: machine learning and deep learning. These two terms are often used interchangeably and the difference between machine learning and deep learning remains unknown to most. Machine learning and deep learning are entirely different from one another.
To simplify, deep learning is a part of machine learning. By definition, machine learning is an approach of AI that is explicitly programmed to make devices understand, analyze, learn, and adapt to their work environment. For instance, Netflix offers us a list of movie recommendations based on our past preferences. Machine learning algorithm parses the assimilated data and its algorithms analyze it to act accordingly. Furthermore, we have come across Google’s ‘did you mean’ section, right?, which pops up when a typo error occurs. Google uses its machine learning algorithm to learn from our mistakes and recommend the correct word to us.
On the other hand, deep learning is a part of machine learning that parses massive volume of data using neural networks to obtain a more profound outcome that machine learning fails to achieve. In simple words, deep learning algorithm works similar to how humans interpret and understand a situation. For example, when it comes to identifying a dog, deep learning technique applies its algorithm that finds out that a dog image has been provided to them, whereas machine learning just parses the data and outputs that it is an animal.
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Source: BBN Times
