Deep Learning vs Machine Learning: Know The Differences

Deep Learning vs Machine Learning: Know The Differences

To the general public, the words deep learning and machine learning are mere interchangeable terms constantly used in the vast world of AI. This couldn’t be farther from the truth. The applications of deep learning and machine learning can be witnessed all around us, which makes it all the more important to know what’s what. This article will take you through the world of AI and help answer the question; deep learning vs machine learning, what’s the difference between the two?

Deep Learning

A major point to remember before diving into deep learning vs machine learning is that deep learning is considered to be an evolution of ML. A deep learning model is designed to continually analyze data with a logical structure similar to how a human would draw conclusions. To precisely complete this analysis, the deep learning model uses something called an artificial neural network. The artificial neural network is a structured layer of algorithms inspired by the network of neurons in the human brain. 

Ensuring that a deep learning model works as intended is a tricky process and it requires an ample amount of training to get the entire process right. But once up and running, it is often nothing short of a scientific marvel that many consider to be the backbone of true artificial intelligence.

Machine Learning

Machine learning is a subset of artificial intelligence and offers systems the ability to learn and improve from experiences without being programmed to such heavy extents. What is often meant by saying that something is capable of “machine learning” is that can perform a function with the data given to it and get progressively better over time. There are two broad categories of machine learning; supervised and unsupervised machine learning.

Supervised learning requires substantial human participation. The computer is fed training data and a model is explicitly designed to ‘teach’ it how to respond to the data. Unsupervised learning surpasses certain limitations and uses unlabeled data in its processes. The system is given the freedom to find patterns and associations as it sees fit, which results in the generation of results that might have been unapparent to a human counterpart.

Deep Learning vs Machine Learning

The following are some of the major differences between deep learning and machine learning:

  • Machine learning uses structured data, whereas deep learning uses ANN for its data representation.
  • Machine learning is the subset of AI and superset of deep learning.
  • Machine learning uses automated algorithms that turn to model functions and predict future action. Deep learning uses neural networks to pass data through various processing layers to interpret data features and relations.
  • A major point to keep in mind while discussing deep learning vs machine learning is that machine learning requires more human intervention than deep learning.
  • Machine learning programs are often less complex than deep learning algorithms, which means that the former can run on conventional computers quite easily, while the same cannot be said for the latter.

Deep learning vs machine learning is a discussion that everyone who ventures into the world of AI must have come across at least once in their careers. Understanding the difference between the two can provide you with a solid foundation upon which you can build your castle of knowledge. We at Praxis take pay keen attention to ensuring that our students are well versed in all things needed to start a career in the world of data and AI. And towards this, we have developed a carefully curated PGP in Data Science. This program will equip you with everything needed to kickstart your career and make your mark in the professional world.

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