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Machine Learning is the new Karma

While on the other side of the coin,

Deep learning is changing the way we live

Analytics, artificial intelligence (AI) and big data – these conversations are no longer complete without the term “deep learning”.

While both the terms are an application of Artificial Intelligence, they both differ depending on their ability for a specific task

To get a better idea on how much the world has hyped them, let's check out these insights.

So, what are these buzzwords that still dominate the conversations about A.I. ?

Don’t worry if they sound mysterious to you right now!

Let’s have a look at some common as well as simplest definition of Machine Learning as well as Deep Learning:


An application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. 

Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

With new added data and experiences, Machine Learning algorithm gets better and better analysis and patterns.

Some real-world examples include:

  • Web page classification
  • Spelling correction
  • Fraudulent Detection


Deep learning, on the other hand, is a subset of machine learning and one of the most popular forms of machine learning algorithms that anyone can come across.

It imitates the workings of the human brain in processing data and creating patterns for use in decision making

They use neural networks, connected like a web which are a family of models similar to biological neural networks in humans.

Some real-world examples include:

  • Image Classificatio
  • Voice Recognition
  • Language Translation

So, what separates deep learning from a normal Machine Learning Algorithm?

The digital era has brought about an explosion of data in all forms and from every region of the world. This data, known simply as Big Data.

And with enormous Data comes enormous problems which traditional machine learning may or may not solve

Everyone knows that Machines are better than people at tricking series of numbers, but what about tasks that are more complicated? How do you train a machine to recognize what a car looks like? Or how to play a complex strategy chess game? Or how to predict the next word in a sequence? and even make predictions about the stock market? Many of these most difficult tasks in artificial intelligence are far more obsolete than the capabilities of a normal machine learning techniques. In these cases, Data Scientists turn to Deep Learning.
Because they make use of an architecture inspired by the neurons in the human brain. 

Although, Number of neurons in human brain can be matched memory-wise but the amount of parallelism to simulate it in real-time cannot yet be achieved

A.I. is used across all industries for several different tasks. Commercial apps that use image recognition, open source platforms with consumer recommendation apps, and medical research tools that explore the possibility of reusing drugs for new ailments are just a few of the examples.