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Deep Learning

With the aid and advancement of technology, we have witnessed Deep Learning application spreading across all domains and spheres of life.

Artificial Intelligence, Machine Learning, IOT, Blockchain are considered as the next big thing in the world of technology and the same applies for Deep Learning.

From Snapchat filtering to image-voice recognition, and from medical image processing to stock market prediction, all these are the use cases of Deep Learning. 

Artificial intelligence, Machine Learning and Deep Learning are some of the buzz words which hovers in everyone’s mind these days. Well,  you might know all these are interconnected but lets jump on to exact essence of the each:

ARTIFICIAL INTELLIGENCE:  AI implies getting  a computer to mimic human behavior in some way.

MACHINE LEARNING: ML is a subset of AI and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.

DEEP LEARNING: Deep Learning is a subset of ML that enables computers to solve the complex problems.

Lets deep dive into the crux of Deep Learning:

What is Deep Learning?

Deep Learning essentially and effectively revolves around Machine Learning technique in which a system takes the input in the form of images ,audio ,text through layers in order to predict the output.

The main crux behind deep Learning is the way the human brain is able to filter information, Communicate with each other to give the final output.

In the human brain, there are about millions and billions of neurons and each neuron is connected to its neighbours.

Essentially, that is what we’re trying to create, but in a way and at a level that works for machines.

An Intuition behind Deep Learning is

You get input from observation, you put your input into one layer that creates an output which in turn becomes the input for the next layer, and so on. This happens over and over until your final output signal!

Deep Learning Models can be classified as 

  • Supervised
  • Semi-Supervisied
  • UnSupervisied

Supervised Learning

Is a function that maps input and gives an output. It works with labelled training data. Essentially, each example is a pair that is made up of an Input data(usually a vector) and the output value that you expect (supervisory signal). In a nutshell, it looks at stuff with labels and uses what it learns from labelled stuff to predict the labels of the other stuff.

For instance:

Same cycle is applied to classification of email as spam, recognising voices in an audio and  creating a model from speech to text.

Semi-Supervised Learning

Your elders strive to imbibe a sense of inculcating good manners and moral values in you. At the same time, there is a volley of stuff which you learn by yourself by observing. This is a classic example of Semi-Supervised Learning wherein semi-supervised learning incorporates both -the labelled and unlabeled data for training.

Semi-supervised learning has been trailed, test and proves that is better than unsupervised learning. 

Unsupervised Learning

Revolves around the relationships between elements in a dataset and able to give output without the usage of

There are scores of deep learning techniques, say Convolutional Neural Network, Recurrent Neural Network, GAN etc

Lets understand main crux of DEEP LEARNING by application of CONVOLUTIONAL NEURAL NETWORK:

How our brain classifies an image!

In the above photo, its pretty difficult to decide if the person is a girl or an old woman

All the above images are addressed to understand that our brain functions on the features of the image it sees and then classifies it accordingly.

In a similar manner, neural networks work. We can see in the image below, the neural network has successfully classified cheetah and bullet train but was unsuccessful in predicting hand glass. This is because of the unclear features in the image.

In simple words, Neural Networks works exactly like a human mind.

Deep learning Process

Deep neural network crystal clearly provides accuracy at an amazing level. They can learn automatically, without predefined knowledge explicitly through coding.

A deep neural network provides accuracy in many tasks.

They can learn automatically, without predefined knowledge explicitly coded by the programmers.


To grasp the idea of deep learning, imagine a family, with an infant and parents. The toddler points objects with his little finger and always says the word ‘cat.’ As its parents are concerned about his education, they keep telling him ‘Yes, that is a cat’ or ‘No, that is not a cat.’

The infant persists in pointing objects but becomes more accurate with ‘cats.’ The little kid, deep down, does not know why he can say it is a cat or not. He has just learned how to hierarchies complex features coming up with a cat by looking at the pet overall and continue to focus on details such as the tails or the nose before to make up his mind.

A neural network works quite the same.

Each layer represents a deeper level of knowledge, i.e., the hierarchy of knowledge. A neural network with four layers will learn more complex feature than with that with two layers.

The learning occurs in two phases.

  • The first phase consists of applying a nonlinear transformation of the input and create a statistical model as output.
  • The second phase aims at improving the model with a mathematical method known as derivative.

Lets take an example to understand it comprehensively.

Suppose a model wants to learn how to dance. After 20 minutes, it will be a random scribble.

After 48 hours of learning, the computer masters the art of dancing.


Suppose you want to model to show a human is dancing.

Now after 20min, you will find it’s not completely trained. You will find only his hands moving without any other reflexive movement.

After 48 hours, you will find that the whole dance movement of the person who is trained.

This apparently reflects that Deep Learning takes time but the results are highly impressive in terms of accuracy of the model

AI in Business

Currently, AI is one of the most prudent means of customer demand and service management.

From movie recommendation in Netflix to upgrade speech recognition in call-centre, Deep Learning has pl

From medical Image recognition to advanced movie recommendation. Fintech companies are growing even faster as they are using DL to save time and reduce cost.

Future Scope

The future of DL is endless and might go till eternity. 

  • DL  with the aid of libraries such as Tensorflow might be integrated with Mobile Development .
  • DL along with GPS will be really lethal 
  • Self driving cars will become really common in future.


Hence, Deep Learning Is Undeniably Mind-Blowing

Neural networks were invented in the 60s, but recent boosts in big data and computational power made them actually useful. A new discipline called “deep learning” arose and applied complex neural network architectures to model patterns in data more accurately than ever before.The future is DEEP LEARNING and the demand of it is at its peak in the market and will be rising for the next few decades.

It’s the trend of the future and one should really learn, understand and apply it in real life.

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