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Deep Learning Fundamentals Interview questions and Answers

  • October 28, 2022
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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  • What is the difference between Deep Learning algorithms and Traditional Machine Learning algorithms?

    • a) Deep Learning algorithms have interpretability whereas Traditional ML algorithms do not have interpretability.
    • b) Deep Learning algorithms perform automatic extraction of features whereas Traditional ML algorithms expect manual extraction of features by using additional image processing algorithms
    • c) Deep Learning algorithms handle unstructured data whereas Traditional ML algorithms handle structured data
    • d) Deep Learning algorithms usually run on CPUs whereas Traditional ML algorithms usually run on GPUs

    Answer - b) Deep Learning algorithms perform automatic extraction of features whereas Traditional ML algorithms expect manual extraction of features by using additional image processing algorithms

    Deep Learning algorithms are majorly used to handle unstructured data, however, they are also used to learn patterns from structured data. One of the major advantages of the Deep Learning algorithm is to perform automatic feature extraction. It will learn the features, which are nothing but weights (also called parameters or coefficient values). Even very complicated features or patterns from images and text data are extracted with ease because of the way the algorithm is developed. The flip side is that additional computing is needed to run deep learning algorithms and GPUs (Graphical Processing Units), which are much faster than typical CPU machines, are used to run the deep learning algorithms. Another drawback is that deep learning algorithms do not have interpretability hence they are also called Black Box techniques.

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