Demystifying Deep Learning

Demystifying Deep Learning

I consider myself strong in algorithms, data structures and programming. During the last few years, my interest to become an expert in deep learning technologies peaked.

My initial understanding was that to be a good consultant in deep learning, I need to learn too many things to make the application work, write monstrous code using a lot of APIs, understand lot of mathematics (calculus, algebra and probability) to grasp the concepts. But my passion to master this new technology made me jump into it a year back. Then there was no looking back.

I have just ended a semester teaching masters and PhD students a course on “Deep Learning”. A few weeks ago, I was able to successfully lead an International workshop on “deep learning” and able to test that how quickly people can kick start their journey of deep learning Technologies. Also, I was also able to consult few startups on deep learning.

I was wrong in my understanding of the complexity of deep learning technologies, because it turned out that it is very easy to learn and master these technologies. Very little math is required. Wonderful courses, Tutorials and help are available online. Deep learning platforms hide the complexity from the programmer, that results in a simpler code. You will be surprised to see the size of the code of various Deep learning projects, if you compare them with classical applications and projects of computer Science. Soon, we will have tools where users will be able to play with various hyper parameters of any application they want to develop; without worrying about the inner details of the black box. So, I invite young generation in colleges and universities to grasp this opportunity for their own benefit and start learning machine learning technologies.

What I learnt from my learning journey is the immense power of deep learning technologies. It has the hidden and unexplored potential in many fields of life. From the technical point of view, it only looks like a function approximation. But, people are awestruck when they found that it can do real time language translation and do real time driver less vehicle manoeuvring with simple weight optimisation using gradient descent methods. Now, people are exploiting it for all possible applications in every walk of life. For me the real use lies in Health Care. If the developer and researcher community along with the industry can spend more time in perfecting the deep learning techniques for detection, analysis and diagnosis of diseases, then it will be a game changer. It can be another revolution which can further increase the life expectancy on earth. Early results have already started coming in various Research journals and articles. To take these innovations to the level so that the common man has access to it, will take time and efforts.

It is natural that companies are spending more resources in the applications, which have commercial benefits to them in the short and long term. For example: targeted advertising, product recommendations, improving the customer experience at the point of sale websites etc. Now, the trend has started taking a new turn and many start-ups are using these technologies for online education, precision agriculture, aids for specially-able people, policy making of states and nations, finding patterns in financial transactions, security and maintenance logs of automation systems etc.

As the more advanced areas of auto encoders and deep generative models are taking shape, it will be exciting to be a partner in this journey of deep learning.

Already the number of data sets that have been made available as open-source are increasing. But, the expectations of developers are a lot more from institutions, government and industries. Early these organisations shed their resistance to make the data available, better it will be for the developers and researchers to come out with tangible solutions to many of the world’s problems. It is in the interest of everybody that we start sharing data to unlock the hidden potential of deep learning. We shouldn't worry too much about privacy issues as there are techniques and methods that keep data anonymous without any trade-off. 

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  • Michael Pell

    Thank you, really looking forward to know more about deep learning

  • Ryan Davies

    Thanks for simplifying Deep Learning which the world is trying to make more complicated then it actually is

  • Bryan Cardenas

    Great Work

  • Justin Francis

    Wonderful explanation, I was looking for something like this for months.

  • Andrea

    Thanks a lot! As a data scientist, I find this post very useful for the lay audience.

  • Kirill Kochubey

    Hi.. I am a non techie but this post helped a lot in understanding the subject of deep learning.

  • David H

    Thank you very much for sharing your wonderful story with us

  • Oscar Alsing

    You are awesome, this is exactly what I need now to improve by knowledge of deep learning technologies :) Thank you!

  • Steve Lee

    I was actually looking for a super simple, basic explanation of how deep learning works. But if you can explain all of that simply and clearly, then why not!

  • Mustapha

    Your content is lovely and everything else you said is also cool

  • Anton Van Goethem

    As a beginner, I have read articles on deep learning and tried to understand what it is and why does this idea sound like a technological boom. Your piece seems to solve this problem, I have a concrete idea about it.

  • Kirill Berezin

    This looks great! I'm keen to check out more

  • Dan C

    What's the point of learning about deep learning without the math, or code? Maybe it's because I'm from a physics background, but to me technical topics usually only make sense once I've understood the math behind them

  • Subir Verma

    This is deep

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Deepak Garg

Tech Expert

Deepak is a Professor and Department Head of Computer Science and Engineering at Bennett University, Greater Noida. He is also a chief consultant for, a resource for algorithms. Deepak is considered as one of the best Algorithm Gurus in India. His active research interests are designing efficient deep learning algorithms. He is Senior Member of IEEE, Senior Member of ACM and Life Member of CSI, IETE. He served as chair of IEEE Computer Society, India Council from 2012-2016 and on the Board of governors of IEEE Education Society, USA (2013-15). He previously worked as a Software Engineer in IBM Corporation Southbury, CT, USA. Deepak has 100+ publications in various International Journals and conferences with 500+ citations and Google h-index of 13. In his 20 years of experience, he has delivered 200+ invited talks across India. He is an ABET PEV and serves on ABET criteria review committee of Computing Accreditation Commission. He is also an active blogger in the Times of India with the tag name of “Breaking Shackles” and is passionate about transforming the landscape of Higher Education. For more details about his accomplishments: Deepak holds a PhD in Computer Science and Engineering from Thapar University.


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