Best Practices to Train Accurate Deep Learning Models: A General Methodology

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 performance 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 study, 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.

@article{Nogales2026,
  title     = {Best Practices to Train Accurate Deep Learning Models: A General Methodology},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {2},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Alberto Nogales and Ana M. Maitín and Álvaro J. García-Tejedor},
  doi       = {10.14569/IJACSA.2026.0170206},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170206}
}