“Transfer Learning” for Bridging the Gap Between Data Sciences and the Deep Learning

Ayesha Sohail
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Ayesha Sohail: Comsats University

Annals of Data Science, 2024, vol. 11, issue 1, No 14, 337-345

Abstract: Abstract Over the past two decades, the community of data science, computer vision and programming has evolved rapidly and new programming techniques have replaced the computationally expensive techniques. This is achieved with the aid of smart programming languages, smart computers and intelligent minds. The neural networks are replaced by the deep neural networks which are comprised of several layers and neurons, the direct large data “classification” has been replaced by the transfer learning tools, which are computationally more efficient and accurate as long as the user has the clear vision of synchronizing the new problem with the pre-trained model. Artificial intelligence tools are much improved since the discovery of transfer learning tools and the programming time of several days or weeks for the deep networks has now reduced to few minutes or hours. This article presents detailed insight of transfer learning frame work with the aid of some useful programming tools.

Keywords: Artificial neural network; Time series; Transfer learning; Pre-processing; Randomness; Training algorithms (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-022-00384-x

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