Lightweight Deep Learning: Introduction, Advancements, and Applications

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Author(s): Hari Kishan Kondaveeti (Vellore Institute of Technology, India), Valli Kumari Vatsavayi (Andhra University, India), Srileakhana Mangapathi (Vellore Institute of Technology, India)and Reddy M. Yasaswini (Vellore Institute of Technology, India)
Copyright: 2023
Pages: 18
Source title: Advancement in Business Analytics Tools for Higher Financial Performance
Source Author(s)/Editor(s): Reza Gharoie Ahangar (Lewis University, USA)and Mark Napier (Lewis University, USA)
DOI: 10.4018/978-1-6684-8386-2.ch012

Keywords: Accounting and Finance / Business Science Reference / Data Analysis & Statistics / Library & Information Science

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Abstract

Lightweight deep learning is a subfield of artificial intelligence and machine learning that prioritises efficiency and compactness while developing deep learning models. It is ideal for low-powered mobile phones, embedded systems, and internet-of-things devices due to their speed and low latency. To make lightweight deep learning models, pruning and quantization are used to remove unnecessary parameters and reduce model weight accuracy. Transfer learning is used to fine-tune a pre-trained deep learning model on a smaller dataset. This chapter introduces the fundamentals of lightweight deep learning, including various lightweight models and their applications across different industries.

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