Akademik Veri Yönetim Sistemi
A to Z of Deep Learning and AI, CRC, Ghent, Belgium , ss.65-71, 2025
The purpose of this paper is to discuss the importance of data in deep learning and to describe the process of finding and preparing instructive deep learning datasets. The process of building the dataset and enhancing the quality of data relevant to deep learning activity is described. The purpose of this methodology is to construct and enhance the dataset that will serve the project™s purpose. Data collection processes, augmentation techniques, and data ethics are some of the collected dimensions. These dimensions were selected based on their practical relevance. Results Sample deep learning datasets for speech and image processing were built considering ethical aspects of data use. The state-of-the-art in data gathering, data gathering tools, sample deep learning datasets, and model accuracy in various domains served as benchmarks for deep learning model efficiency. Conclusion Researchers and developers of applications that apply neural networks must take into account the importance and scale of the data when training their models. Moreover, there is plenty of untapped potential regarding collecting, synthesizing, and using quality deep learning datasets.