The Basic Difference Between Deep Learning and Machine Learning

Posted By :Rajat Soni |31st August 2021


Understanding the latest innovations in artificial intelligence (AI) can be challenging, but if you're only interested in studying the basics, many AI discoveries can be simplified down to two concepts: machine learning and deep learning.

These concepts can appear to be interchangeable buzzwords, that is why it is essential to understand the differences.

And those comparisons should be understood—machine learning and deep learning examples are all around us. It's how Netflix decides which show you should watch next, how Facebook determines whose face is in a photo, and how self-driving cars become a reality, and how a customer service representative figures out if you'll be satisfied with their service before you even fill out a customer satisfaction survey.


Deep learning vs. machine learning

The simplest way to grasp the fundamental difference between machine learning and deep learning is to realise that deep learning equals machine learning.

Deep learning is a significant improvement in machine learning. It makes use of a programmable neural network, which allows robots to make accurate judgments without the need for human intervention.


Key Differences:

1.Deep learning is a subset of artificial intelligence that is a sort of machine learning.

2. Machine learning refers to computers learning to think and act without the need for human involvement, whereas deep learning refers to computers learning to think utilising structures that are modelled after the human brain.

3. Deep learning usually requires less ongoing human intervention than machine learning, which requires less computational resources.

4. Deep learning has the ability to interpret photos, videos, and unstructured data in ways that machine learning does not.


What is machine learning?

Machine learning is a branch of artificial intelligence that involves algorithms that parse data, learn from it, and then use what they've learned to make better decisions.

An on-demand music streaming service is a simple example of a machine learning system. Machine learning algorithms correlate the listener's preferences with other listeners who have similar musical tastes, allowing the service to make decisions about which new songs or artists to propose to a listener. Many services that provide automated suggestions use this method, which is commonly referred to as AI.


What is deep learning?

Deep learning is a sort of machine learning that creates a "artificial neural network" that can learn and make decisions on its own by layering algorithms.


Difference between machine learning and deep learning

Deep learning is a subset of machine learning in practical sense. In fact, deep learning is a type of machine learning that works in a similar fashion to traditional machine learning.  Its abilities, on the other hand, are somewhat different.

Simple machine learning models improve over time at whatever task they are assigned, but they still need to be monitored. If an AI algorithm makes an inaccurate forecast, an engineer must step in and make improvements.  With a deep learning model, an algorithm can use its own neural network to decide whether a prediction is correct or not.


How does deep learning work?

A deep learning model is designed to analyse data in real time using a logic framework similar to that used by humans to reach conclusions. Deep learning applications accomplish this by using an artificial neural network, which is a layered structure of algorithms. An artificial neural network's design is based on the biological neural network of the human brain, resulting in a significantly more capable learning process than ordinary machine learning models.

Google's AlphaGo is a wonderful example of deep learning. Google developed a computer programme that learned to play the abstract board game Go, which is notorious for needing keen intellect and intuition. AlphaGo's deep learning model learnt how to play at a level never seen previously in artificial intelligence by playing against professional Go players, and it did it without being directed when to make a specific move. When AlphaGo defeated many world-renowned “masters” of the game, it created quite a stir—not only could a machine grasp the game's complicated strategies and abstract components, but it was also becoming one of its best players.


To summarise the differences between the two, consider the following:

Machine learning is a technique for analysing data, learning from it, and making effective decision based on that data.

Deep learning separates algorithms into layers, creating a "artificial neural network" capable of learning and making decisions by itself.

Deep learning is a subfield of machine learning. While both are classified as artificial intelligence, deep learning is the key to creating AI that is as human-like as possible.


About Author

Rajat Soni

Rajat Soni is working as a Frontend Developer with approx 2+ years of experience and holding certification in the domain. He is skilled in AWS services ( EC2, S3 bucket, Open search), HTML/CSS, ReactJS, Client Management, Solution Architect and many more related technologies. He has been a part many successful projects namely Konfer project, where he has migrated the project from Angular js to Angular 12 , Virgin Media Project, I-Infinity project & many more along with leading the team to handling the project end to end.

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