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Deep Learning Fundamentals Interview questions and Answers

  • October 28, 2022
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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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