Let’s Understand the Difference between Machine Learning Vs. Deep Learning

 In Our Thinking

AI or Artificial Intelligence has two subsets that are much talked about in the digital sphere. These subsets are Machine Learning and Deep Learning, and often, these two terms are used interchangeably.

For those who are not in the know till now, here are the definitions which will help you understand the basics:

What is Machine Learning?

Machine Learning is associated with algorithm creations. It doesn’t involve human intervention for creating the desired output. It can be altered on the basis of structured data.

What is Deep Learning?

Deep Learning is a subset of machine learning. It is where the algorithm creation occurs, but these are characterized by multiple layers of algorithms. This leads to a different interpretation of the data fed. This network of the algorithm in deep learning is also known as Artificial Neural Networks.

Now that the definition is somewhat clear, let’s move to the next step, and that is to understand the fundamental differences between these two via the following pointers:

  • Deep learning, although being a subset of machine learning, functions differently. The algorithm, in this case, can assess and determine the accuracy of a prediction via its own neural network. This is not the case with machine learning, and it still requires some amount of human intervention or adjustments when the algorithm leads to an inaccurate prediction.
  • How the data is presented in the system forms the crux of the difference between machine learning and deep learning. Think structured data vs. ANN (Artificial Neural Networks). This is also the reason why machine learning is restricted to solve only simple queries, while deep learning can take better and more complicated decisions that generally involve massive data.
  • Deep learning models continuously analyze data and make intelligent decisions. Machine learning, on the other hand, depends on algorithms that can parse data and then learn from the data. Based on the learnings, it takes informed decisions.

That being said, let’s get more clarity with the following examples that explain the applications of machine learning and deep learning.

Examples of Machine Learning

No write-up on machine learning is complete without mentioning the big data analytics that assists Netflix to predict what its customers are going to enjoy watching. Based on this continuous data from its users, the platform is able to come up with several popular shows and original movies that continue to rise in terms of popularity. No wonder, it allows them to commission several seasons of new shows year after year!

Along the same lines, machine learning is also applicable in image recognition. Several platforms are using it for face detection in an image such as Facebook. This same technology comes to play when it comes to recognizing different characters for distinguishing between two sets of handwriting.

It also drives another crucial technology that powers voice-based assistants that are becoming increasingly commonplace nowadays – speech recognition. A software application can be used to recognize all the words being spoken in an audio clip or a file. It is then converted in a text file. The speech signals are comprehended in this situation and can be segmented accordingly.

Examples of Deep Learning

The most obvious and famous use of deep learning is that of automated car manufacture. It can be used to detect crucial objects during driving, such as traffic signals and stop signs. Another important aspect is that it can detect pedestrians and therefore, decrease the number of road accidents that may take place.

The next application of deep learning on our list is the automatic colorization of monochrome images. This feat is achieved by using the objects in the photograph or image and then using the context to color the image. The technology involves widespread neural networks that have been trained for image colorization using multiple layers. The end result is a recreated image with all the colors in it.

What are the latest trends in machine learning and deep learning?

As machine learning and deep learning go hand-in-hand, here are some of the trends that we are most likely to see in the near future (or see better versions of the same):

  • Transfer learning
  • Better approaches in cyber security
  • Robotic process automation
  • Transparent decisions
  • Edge intelligence
  • Quantum computing
  • Cloud computing

The Wrap

There are numerous fine nuances that divide machine learning and deep learning, although both are tied to similar principles of AI. But the crux is that the former includes more complex code while the latter leads to more enhanced results.

In any case, with the passage of time, both these AI subsets are going to evolve, and we got to wait and watch in the sidelines what do they bring to the table in various industries.

As the quote by Chris Duffey goes, “The only limit to AI is human imagination.” We rest our case.

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