What is deep learning? an overview

Deep learning basics

Deep learning is actually an artificial intelligence function with immense capability to find out the hidden pattern within a huge amount of data generated in this era of data explosion. It is an advanced learning system which mimics the working principle of the human brain. Such kind of vast unstructured data is not possible for the human being to analyze and draw some conclusion. So, such a learning procedure has been proved very helpful to make use of big data.

According to Andrew NG the founder of deeplearning.ai and popular Coursera deep learning specialist

Deep learning is a superpower. With it you can make your computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. If that is not a superpower, I don’t know what is.

Andrew NG

In this respect, machine learning is a much bigger domain and deep learning can be considered as a subdomain of it. Deep learning relates to the deep neural network and mainly works in an unsupervised manner.  The network is popular with the name Artificial Neural Network as it mimics our brains vast network of neurons.

A schematic diagram of neural network with two hidden layer

Difference between machine learning and deep learning

 The main difference between these two processes lies in the feature extraction process from images. Feature extraction is the basic component of both the processes.

Feature extraction

But the difference is machine learning requires to perform this process manually and then feed this information in the model. Whereas in deep learning the feature extraction happens automatically and provided to the network to match with the object of interest. In this context, deep learning is called an “end-to-end” process of learning.  

Resource intensive

Another major difference between these two processes is data processing capability. Deep learning can make good use of a huge number of labelled data provided you have sophisticated Graphics Processing Unit (GPU). On the other hand, machine learning has different modelling techniques to give you a good estimate even with a less number of labelled data. 

Scaling with the data

Deep learning has a big plus point over machine learning which makes deep learning far more accurate than machine learning is that deep learning algorithm scales itself with the data. That means as we use more images to train the deep learning algorithm it will give the more accurate result.

But it is not the case with machine learning algorithms. Machine learning algorithms attain a plateau after a certain level of performance achieved. It will not improve even with more training after this level. See the below image to realize the difference.

Deep learning  improves as the data size increases
Deep learning improves as the data size increases

So, now the question is which approach should be used by one? To answer that I would suggest that it depends on your situation i.e. the type of problem you desire to solve, the GPU capacity available to you and the most important factor how much labelled data you have to train the algorithm.

Deep learning is more accurate than machine learning but more complex too. So unless you have thousands of images to train it and high-performance GPU to process such a large amount of data, you should use any machine learning algorithm or combination of them.

Deep learning: the working principle

Deep learning mainly relies on Artificial Neural Network (ANN) to unravel the wealth of information from big data. So, it is very interesting to know how does this process is actually performed. 

If you have a little exposure to the traditional modelling process, you may have an idea that conventional regression models suffer from various limitations and had to fulfil several assumptions.

And most of the cases it performs not so well in capturing the real nonlinear nature of real-world data. This is mainly because of the traditional regression way of modelling does not attempt to learn from the data. This learning process which makes the real difference between these two approaches. 

The term deep in deep learning suggests the mechanism of information processing through several layers. Deep learning or deep structured learning is mainly based on a learning process which is called representation learning. It is actually finding out representations for hidden feature or pattern recognition from raw unstructured data.

Accuracy of both the approaches

The accuracy deep learning attains in its estimation is impeccable. The applications mentioned above needs to be very precise to satisfy end user’s expectation. And such accuracy can only be provided by deep learning. To achieve such accuracy, it trains itself using the labelled data to continuously improve the prediction by minimizing error. 

So, the amount of labeled data is an important factor determining how perfect is the learning process. For example to make a perfect self-driving car we need to train the algorithm with a huge amount of labeled data like images and videos of road, traffic, people walking on the road or a busy road at any time. 

No doubt deep learning is a very computation-intensive process. Processing such a huge amount of image and videos and then using them to train the algorithm may take days and weeks altogether. 

This was one of the key factors that in spite of the concept of deep learning came back in 1980 it was not in much use till recently. It is just because back then researchers were not equipped with systems with high computing capacity. In today’s era of supercomputers or systems with high-performance GPUs, advanced cloud computing facilities have made processing such enormous size of data possible within hours or even less. 

ANN: mimicking the human brain

Artificial Neural Network or ANN as the name suggests it mimics our brains working principle. In our brain, the neurons are the working unit. The network between the innumerable neurons works as layers in carrying the sensation from different body parts to the designated part of the human brain. As a result, we can feel touch, smell the fragrance, taste a tasty food or hear music.

The learning process

Human being basically learns from past experience. Since childhood, a person gathers experience about everything in his surrounding and thus learn about them. For example, how can we able to identify a dog or cat? Perhaps because we have seen a lot of these animals and learned the differences between their appearances. So, now for us chance of making a mistake in identifying these animals is almost nil.

Deep learning for feature extraction

This is the very nature of the learning process of a human being. The first time a baby sees a dog he/she knows it is a dog from the parents. Gradually through this process, the baby comes to know animals of this appearance are called dogs. This learning process of a human being is exactly followed in case of deep learning. And the result gets more and more accurate as the learning process continues.

ANN also comprises of neurons and the nodes are connected to form a web-like structure. There can be multiple layers of such neurons (generally two to three but theoretically it can be any number). These layers pass different information from one layer to another and finally produces the result.

One layer of neuron act as the input layer for the immediate next neuron layer. The term “deep” actually signifies the number of layers only in any ANN. The most common and frequently used deep learning network is known as a Convolutional Neural Network (ConvNet/CNN).

Convolutional Neural Network (CNN)

CNN is one popular form of deep learning. It actually eliminates the need for manual feature extraction from any image in order to recognize it. CNN used thousands of images and used hundreds of hidden layers for its feature extraction to match it with the object of interest.

The hidden layers are arranged in such a fashion to recognize features with a higher order of complexity. That means the first hidden layer may recognize only the edges of the image whereas the last one will recognize a more complex shape of the image which we desire to recognize.

Applications of deep learning

Today’s digital era is collecting and generating data at every moment of our presence in the digital world. Be it social networking sites, online shopping, online movies or online study/research anything. We are providing lots of data as input to get the desired output from the internet. 

The data size is enormous and also completely unstructured but has a lot of information on it. Which if analyzed properly can help the government to take better policy decision or business houses to frame an effective business plan.

Since last few years, this learning process has been the key concept behind some revolutionary ideas and applications like:

Image colourization

Recently some of the old black & white movies are relaunched with colour effects. If you watch them you may be surprised by the precision and accuracy of the colourization process. Artificial intelligence has made this task possible to complete within a few hours. It was previously only possible with human skills and hard labour.

The famous movie “Pather Panchali” of the Oscar-winning film director Shri Satyajit Ray was shot in black & white. Recently Mr Ankit Bera, Assistant Research Professor of AI at the University of Maryland in U.S. has done an experiment to colourize the movie and the result is really impressive (read the full story here). Here is a comparison of two still frames side by side from the movie for your comparison.

A screenshot from the report

Self-driving car

A self-driving car where deep learning enables a car to recognize a stop sign or to distinguish a pedestrian from a lamppost, judges the situation of a busy road and thus reducing the chance of an accident.

An autonomous car is able to take human-like decisions on the basis every probable situations you may face while driving. It is still in the testing phase. It improves its perfection as it gets trained with more data of real-life traffic condition.

Facial recognition

Its presence is now everywhere, be it biometric attendance system, AADHAR enabled transaction or your mobile’s face lock system. This recognition system is so smart that it identifies you even when you have shaved off your mustache or changed your hairstyle.

Natural language processing and Virtual assistants

In natural language processing, speech recognition where deep learning plays a crucial role in following the command given by a person to his smartphone or any smart device. You may also have tried Google’s speech to text tool to save yourself from typing a lot, so thanks to voice recognition also a gift of deep learning.

Different online service providers have launced several virtual assistants mainly on the basis of this concept. You must have heard the names of apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa etc. all are very popular virtual assistants making our daily lives a lot easier.

Language translations

In language translations deep learning has played a big role as in natural language processing. This has benefitted the travelers, business people and many others who need to visit a lots of place and also communicate a lot with people speaking foreign language.


You may have experienced that in recent times whenever you want to contact the customer support or product help of any company to get some questions answered related to any of their product, the first basic question-answer part is generally automated and intelligently replied without any human interventions.

In medical research

It plays a pivotal role nowadays. In cancer research identifying the affected cells. A dedicated team of researchers at UCLA has built an advanced microscope which used deep learning to pinpoint the cancerous cells.

Many industries like drug industries, automobiles, agriculture sector, board game making brands, medical image analysis are actively conducting research with deep learning in their R&D sector. 


So, I think in this article I am able to give you a basic idea about what is deep learning and how it works. Although the idea of deep learning was conceptualized long back in 1986 due to limitation of resources it did not take up. And took more than a decade to come into action. Today we have sophisticated computing devices and no dearth of data. In fact, there are oceans of data with every aspect of our daily life.

Every moment of our life, our life and every activity happening in the world is getting stored in one or other format of data, mainly as images, videos, audios etc. These data is so huge that conventional process of data analysis can not handle it and simple human capacity would take decades to analyze it.

Here comes deep learning with its fascinating power of data analysis mainly pattern recognition. Deep learning works especially with more accuracy when the database is a large number of audio, video or image files, so it is the best fit for the situation. And this is the reason it gets its name as “Deep” as many features of the images get extracted at different layers of the deep learning process. So, deep actually refers to the depth of layers.

Deep learning is vast topic and a single article can not cover all of its aspects. So its basic features are mainly discussed here and you can start your deep learning process with this article.

Follow this blog regularly as many interesting articles regarding deep learning and its application will be posted here regularly. If you have any particular topic in your mind please let me know by commenting below. Also, share your opinion about this article and how it can be improved further.