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I am a researcher in Data Science at the University of Granada. Here are some places where you can find me:

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david [at] deivi [dot] ch

Latest publications

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges
Julián Luengo, Raúl Moreno, Iván Sevillano García, David Charte, et al. Information Fusion.

Revisiting data complexity metrics based on morphology for overlap and imbalance: snapshot, new overlap number of balls metrics and singular problems prospect
Jose Daniel Pascual Triana, David Charte, Marta Andrés Arroyo, Alberto Fernández, Francisco Herrera. Knowledge and Information Systems.

Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study
David Charte, Iván Sevillano García, María Jesús Lucena González, et al. International Conference on Hybrid Artificial Intelligence Systems.

Reducing Data Complexity using Autoencoders with Class-informed Loss Functions
David Charte, Francisco Charte, Francisco Herrera. IEEE Transactions on Pattern Analysis and Machine Intelligence.

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Latest posts

Machine Learning: ¿Qué es y para qué sirve el análisis exploratorio? (con un ejemplo para que lo veas claro)

Reconocimiento y clasificación de imágenes con Deep Learning

Aplicaciones de Inteligencia Artificial y Machine Learning en pequeños negocios

5 aplicaciones prácticas inesperadas de la Inteligencia Artificial

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Other material

Curso Math-ML: Álgebra lineal y reducción de la dimensionalidad

Máster online en Machine Learning y Ciencia de Datos con Python y R

Docencia: Modelos de Computación

Docencia: Estructuras de datos

Autoencoders: Representation learning y aplicaciones prácticas