Why Deep Learning Over Traditional Machine Learning?

Artificial intelligence is on the rise right now and everywhere people are discussing the benefits of machine learning as well as deep learning. There are two popular concepts in artificial intelligence which are machine learning and deep learning, and if you aren’t aware of this then it’s important that you figure out which one is better so that you choose the right technology for your business and it manages to develop and grow more effectively.

While machine learning has been popular for a really long time, these days more and more businesses are leaning towards deep learning mainly because of the high level of accuracy that it has to offer even with the little data that it is provided with. In terms of supremacy, there is no denying that deep learning has a better name and can provide your consumers with more reliable and accurate results in comparison to machine learning.

While most software industries depend on machine learning because of its intelligent concept, using deep learning is better mainly because the set of algorithms that are used in machine learning need to be precise and if not then the results are not as effective as you would like it to be. Deep learning on the other hand manages to make better and more intelligent decisions even without a strong data algorithm that is fed into the system. Although machine learning is smart and it can self teach itself, this takes a long time for the system to adjust and deep learning can be done to improvise from the very first day.

If you always thought that machine learning is more popular than deep learning then you should know that machine learning has just been around for a longer time which is why the concept is more prominent. The first occurrence of machine learning popped up on Facebook with its face recognition software. This took the world by surprise and all businesses started to invest in machine learning to make web services more appealing to the customer.

Other popular brands like Netflix, Google and Amazon began to incorporate machine learning to provide customers with services that were more friendly and interesting. From voice recognition to face tagging, machine learning has been an integral part of the consumer’s life which is why it is most spoken about. That being said, there is no room for error when it comes to deep learning and while this is a relatively new concept it is more likely to overtake machine learning in various ways because of the high levels of Intelligence that it portrays as well as the user friendly platform that it comes with. If you are still not too sure about choosing between machine learning and deep learning then you can get a detailed explanation on the subject by visiting this website.

Since machine learning has been around for a longer time, data scientists and machine learning experts are more comfortable using this technology in comparison to deep learning. But it does not take away from the fact that deep learning is more interesting and is capable of more in comparison to machine learning. If you are looking to mimic the brain of human and replace their intervention in daily routine activities then there’s nothing better than deep learning because it’s more promising and effective. While machine learning still has a lot of room for error, deep learning covers bridges the gap by providing more intelligent, swift and effective solutions that make consumers feel better.

If you thought that deep learning was a separate part of machine learning then you are wrong. It is practically a subset of machine learning that has improvised on the features and solutions that machine learning provided and has manage to become more flexible with limited amount of information. This simply means that you can have a simple concept and limited amount of data to provide and you can still get better and more effective results because of deep learning. It does take a longer time for a data scientist to incorporate deep learning into your entire business module, but once it’s done the system can self teach itself and become smarter with each year which simply means that the system will not need to be replaced.

Machine learning on the other hand needs to constantly be fed data to stay updated with what the business models are and what are the various changes that are added into the business. This means constantly having to hire a data scientist or machine learning experts which is an added expense. This is something that you don’t need to do when you get deep learning.

There are various dark areas in machine learning that require to be categorized and provided the right kind of information and when a consumer covers a dark area it becomes difficult for the system to provide a solution that can prove to be beneficial to the consumer. Many times, it can get annoying and consumers often leave bad feedback which is negative for a business. There are various reasons that could lead to this and this does not necessarily mean that machine learning is a bad thing. What it simply means is that machine learning needs to be extremely accurate and all the information provided for machine learning to work well has to be precise and informative as well as detailed.

This is something that you don’t have to worry about when you choose deep learning. Deep learning is a more advanced version of machine learning and once you incorporate it into the system it can automatically pick up information that it is surrounded by it thereby making it easy for the system to streamline and improvise each day. It can manage to detail out answers from the deepest of sources in the organization making the system stronger and more likely to provide the right answers in comparison to machine learning algorithms. This technology has high expectations and is one of the main reasons why people are looking to develop self driving cars as well as automated homes.

Author: Sohel Ather

One comment

  • Machine Learning is taking over the web. It is trying to penetrate in almost every aspect of human life.
    Deep Learning is one of the branches of machine learning. It deals with algorithms and processing that are inspired by the structure and function of the human brain and neural network.

    Neural networks have multiple layers and each layer has many interconnected nodes. The nodes are the places where actual computation happens and output is fed to next node. Each node is associated with an activation function which decides the output of the node.

    When you build your neural net, you have to train it with large data set. This training will make you model to predict most accurate results when you supply input dataset.

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