DEEP LEARNING: EVOLUTION, INNOVATIONS, AND APPLICATIONS IN THE LAST DECADE

Abstract

Deep learning has become a popular method in the last ten years with its superior performance in many fields, especially in health. Although it is a sub-branch of machine learning, one of the most important reasons for researchers to use this method is that it automates the difficult processes of feature extraction stages in traditional machine learning methods. With the advancement of technology every year, it has become easier to create large data sets or to access large data sets that have been used before on the web. Researchers who want to work on large data sets use deep learning methods effectively because of their advantages instead of using traditional machine learning methods. The foundations of deep learning were laid with the deep belief networks method, which was first developed in 2006. Later, with the significant success of the Convolutional Neural Network (CNN) method developed in 2012 in image classification, deep learning methods have been used in many applications in other disciplines. The success of the deep reinforcement learning algorithm that defeated Alpha Go champion Lee Sedol in 2016, the remarkable success of the contentious generating networks in creating their own unique images, the success of the Siamese networks with the ability to learn from little data in signature verification and facial recognition systems, the success of the artificial intelligence chat bot ChatGPT, which was launched in the last months of 2022, attracted attention in a short time, and the ability of DALL-E, a similar language model, to create images from texts, shows that deep learning is in constant innovation and development. This study aims to give an idea to researchers who will work in this field in the future by talking about the basic concepts of deep learning and the innovative and popular approaches used.