When I was just starting myself, I was super confused between these terms. I used to hear them everywhere. “Another Deep Learning breakthrough”, “Machine Learning engineers created bla bla”, “Deep Learning is the new AI!” And I asked myself, what exactly is Deep Learning? And what exactly is Deep Learning vs Machine Learning? And just like you, I ended up swooshing through various blogs and YouTube Videos only ‘related’ to this topic but not exactly put up as I needed. So guys like me who’re having a hard time, you don’t need to go anywhere else!

First of all, to understand the basic difference “Deep Learning vs Machine Learning”,  you should have a basic and clear idea of what exactly they both mean individually.

Well if we go by Ms.Wikipedia’s definition,

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.

Puzzled? Let’s just simply our definition…

Machine Learning is just another set of algorithms to write a program that opens a completely new world of opportunities for humans. It learns from past data and is able to predict the future! Isn’t that just amazing? And the best part is, without even having to program it explicitly, it can predict pretty accurate results.

And trust me when I say, It’s not like some evil AI robot that’s gonna destroy us. In fact, we are surrounded by AI robots already! Are we dead? I guess not! Personal Assistants like Siri, Alexa, Google Home, fitness trackers like FitBit and many more, what are those? You guessed it right! They’re nothing but ML Programs fed into a device/machine. Let me ask you this now, how else do you define a robot?

Let’s not go off track and into robots and machines and end of the world which is due until March 29th, 2063 (shh..).

But here’s a question, Machine Learning is a field in Computer Science, so how does it differ from traditional computational approaches? Well, the simplest answer would be that the traditional approach uses algorithms that are explicitly programmed and are used by computers to solve problems. Where on the other hand, Machine Learning algorithms allow for computers to train on data inputs and then use statistical analysis to return some predicted values within some range and accuracy.

So now that you have a general idea of what ML is, let’s just dive straight into the topic of this blog.

Deep Learning vs Machine Learning?

This is like asking how is Potato is different from vegetables? Or …or how is a Burger different from Fast-foods? Are you getting my point? No..? Ok, let me elaborate.

See, all Potatoes are a vegetable but all vegetables are not Potato. All Burgers are fast foods but all fast foods are not burgers. All Deep Learning algorithms are a Machine Learning algorithm but all Machine Learning algorithms are not a Deep Learning Algorithm!

That means Deep Learning is a subset of Machine Learning! Therefore, all the properties of a Machine Learning algorithm are carried out by Deep Learning algorithms but not the other way around.

So coming back to our question again, what is Deep Learning anyway?

Let me introduce you to the power of Deep Learning first (deep learning applications), and once you start to finally respect this thing, we’ll move forward.

In 2015, Google DeepMind’s bot ‘AlphaGo’ beats the world Go champion Lee Sedol with the end score of 4–1. And in 2017, the newer version of AlphaGo called ‘AlphaZero’ beats AlphaGo with the end score of 100–0. Can you believe it? 100–0!! I was super surprised when I heard it for the first time. And this was all the magic of Deep Learning.

And they not only stopped there, recently enough they build ‘AlphaStar’ which has beaten the world’s top players of “StarCraft II” which is considered to be more complex than Go.

Cool? Isn’t it? And this was just a single example of the many Deep Learning Example s!

Deep Learning is nothing but the evolution of Machine Learning.

Yes, I agree that a Machine learning model does get progressively better over time in whatever they are being trained on, but they still need some guidance. When the ML model starts giving wrong predictions (high error), an engineer has to step in to tinker with some parameters to optimize the algorithm further, manually.

How does Deep Learning work? Patience my friend..

Whereas, a Deep Learning algorithm is intelligent in its purest form. It fixes its error itself, i.e, it optimizes itself when it starts getting wrong predictions with no human intervention through its own Neural Network.

A Deep Learning model is designed in parallel with a Human Brain, i.e a DL model can analyze data with logic just like a human would draw conclusions. For example, how would you recognize a square? You’ll check for its basic properties. You’ll check it the figure contains exactly 4 lines or not, if the lines are connected or not, if the lines are equal or not, if the lines are perpendicular to each other or not…and a few more things. And then you’ll be able to predict if it's a square or not. And that my friend is the same approach a DL model takes. Surprised?

Deep Learning achieves this using a layered structure of algorithms called Artificial Neural Networks whose design is inspired by the Human Brain (this is how the DL model is designed in parallel with a human brain, and is one of the many types of Deep Learning techniques) which gives it an edge over classic Machine Learning Algorithms. An ML model will not be able to create marvels like AlphaGo or AlphaZero.

Though, a DL model as to outperform an ML model requires a lot more training and a lot more Data. But if given the right ingredients, it can outperform ML models like crazyy!

Who wins in the fight of Deep Learning vs Machine Learning?

Now wondering when to use deep learning vs machine learning?

The primary difference between Machine Learning Deep Learning systems is the way the data is fed to them. ML systems require a structured form of data (mostly) to work on, whereas DL systems just rely on the layers of their network structure (ANNs, DNNs, CNNs, RNNs etc).

Again, ML systems learn on the data fed to them to work out some prediction, but, they need human intervention when the error margin starts to increase i.e the predictions start to move away from the actual results.

This, however, is not a problem faced in a DL system. A DL system is capable enough to work out something from the data thrown at it and even optimize itself without any human intervention when the predictions start to deviate.

Now comes the amount of data. If we don’t have large amounts of data, DL might not outperform ML systems!

But let’s assume the scenario is now reversed. Now we’ve got a huge database, just as seen previously, it’ll be a wiser option to go with DL systems rather than solving it again with ML systems. The real applications of Deep Learning are on a very big scale. They are more suitable to perform heavy and complex calculations.

Problems too complex for Machine Learning can be solved by Deep Learning easily. Though a Deep Learning system to work smoothly requires high computational power for training Deep Neural Networks.

Ok, I got it. What's next?

A very simple answer which you might have figured out by now yourself, Deep Learning is the future!

But again, the most important aspect to keep in mind is that Data drives everything. The quality of data determines how well we can do magic with it. Andrew NG, one of the rockstars in the world of AI said that,

If the AI model is a Rocket Ship’s engine, then the Data is rocket fuel. Without a good amount and quality of rocket fuel (data), we won’t even reach the orbit and without a good engine (AI model), we won’t even take off!

I want to thank Mr. Tapas Mishra, my mentor, my guide and the best big brother! He's the one person without whom I wouldn't even know what I be doing right now. He gave me more than just direction. Thank you bhaiya, for believing in me when I couldn't even do that for myself, for seeing things in me which I could never have figured out on my own. There's nothing I can repay you with for what you've done for me. You're the best.

Next Read: Neural Network in a Nutshell (from Scratch)