Version 1
: Received: 19 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (14:35:23 CET)
How to cite:
Nogales, A.; Maitín, A.; J. García-Tejedor, Á. Best Practices to Train Accurate Deep Learning Models: A General Methodology. Preprints2024, 2024021153. https://doi.org/10.20944/preprints202402.1153.v1
Nogales, A.; Maitín, A.; J. García-Tejedor, Á. Best Practices to Train Accurate Deep Learning Models: A General Methodology. Preprints 2024, 2024021153. https://doi.org/10.20944/preprints202402.1153.v1
Nogales, A.; Maitín, A.; J. García-Tejedor, Á. Best Practices to Train Accurate Deep Learning Models: A General Methodology. Preprints2024, 2024021153. https://doi.org/10.20944/preprints202402.1153.v1
APA Style
Nogales, A., Maitín, A., & J. García-Tejedor, Á. (2024). Best Practices to Train Accurate Deep Learning Models: A General Methodology. Preprints. https://doi.org/10.20944/preprints202402.1153.v1
Chicago/Turabian Style
Nogales, A., Ana Maitín and Álvaro J. García-Tejedor. 2024 "Best Practices to Train Accurate Deep Learning Models: A General Methodology" Preprints. https://doi.org/10.20944/preprints202402.1153.v1
Abstract
In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.
Keywords
Methodology; Artificial Intelligence; Deep Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
