Deep Learning vs Machine Learning: What’s the Difference?

All of a sudden, everyone is talking about Deep Learning and Machine Learning, irrespective of whether they understand the differences or not! Whether you’ve been actively following data science or not – you’d have heard these terms.

Deep Learning vs Machine Learning

machine learning

Machine learning uses a group of algorithms to analyse and interpret data, learn from it and support the understanding, making the best possible decisions. On the other hand, Deep learning structures the algorithms into multiple layers to make an “artificial neural network”. This neural network can learn from the info and make intelligent decisions on its own.

What’s Deep Learning?

The concept of deep learning isn’t new. It has been turned around for a few years now. But these days, with all the hype, deep learning is getting more attention.

Conventional Machine Learning methods tend to succumb to environmental changes, whereas deep learning adapts to those changes by constant feedback and to improve the model. Deep learning is much facilitated by neural networks which mimic the neurons within the human brain and by the embedded multiple-layer architecture (few visible and few hidden).

It’s a complicated sort of machine learning which collects data, learns from it, and optimises the model. Often some problems are so complex, that it’s practically impossible for the human brain to grasp them, and hence programming it’s a far-fetched thought.

Primitive sorts of Siri and Google assistant are appropriate examples of programmed machine learning as they’re found useful in their programmed spectrum. Whereas, Google’s deep mind may be the greatest example of the deep learning process. Essentially, deep learning means a machine that learns by itself by multiple trial and error methods. Often a couple of hundred million times!

Read: 7 Best Laptops for Data Science and Data Analysis

What is Machine Learning?

It is a subset of AI that uses statistical strategies to form a machine that learns without being programmed explicitly using the prevailing set of knowledge. It’s evolved from the study of pattern recognition in AI. In other words, it also can be defined as a subset of AI involving the creation of algorithms that may modify itself without human intervention to supply desired output- by feeding itself through structured data.

When to use deep learning?

  • If you’re a firm with boatloads of knowledge to derive interpretations from.
  • If you’ve got to solve problems too complex for machine learning.
  • If you’ll spend tons of computational resources and expenses to drive hardware and software for training deep learning networks.

When to use Machine learning?

  • If you’ve data that will be structured and want to train the machine learning algorithms.
  • If you are looking to leverage benefits to AI to surge before the competition.
  • The best techniques from machine Learning help within the automation of various business operations, including biometric identification, advertising, marketing, and knowledge gathering and help leverage great opportunities for the longer term.

The vital difference between Machine Learning and Deep Learning

  1. The main difference between deep learning vs machine learning stems from the way data is presented to the system. Machine learning algorithms nearly always require structured data, whereas deep learning networks believe layers of the ANN (artificial neural networks).
  2. Machine learning algorithms were built to “learn” to try to do things by understanding labelled data, and then use it to supply different outputs with more sets of knowledge. However, they have to be retrained through human intervention when the particular output isn’t the specified one.
  3. The networks of Deep Learning don’t require any human intervention because the nested layers within the neural networks put data through hierarchies of various concepts, which eventually learn through their errors. However, these are subjected to flawed outputs if the standard of knowledge isn’t ok.

Since machine learning algorithms require labelled data, they aren’t suitable to unravel complex queries which involve an enormous amount of knowledge.

Over to you

Deep learning is a complicated sort of machine learning which comes in handy when the info to be addressed is unstructured and colossal. Thus, deep learning can cater to a bigger cap of problems with greater ease and efficiency. Through this article, we had got an overview and comparison between deep learning and machine learning techniques.



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