Deep Learning has established itself as the core technology driving today’s artificial intelligence (AI) innovations. It is a method of learning vast amounts of data and independently discovering patterns based on artificial neural networks that mimic the structure of human brain networks. As a subfield of Machine Learning, it enables a much higher level of prediction and classification than traditional algorithms, and is widely used in various industries, including autonomous driving, medical diagnostics, and financial analysis.
The concept of Deep Learning began with the research on perceptrons, which mimic brain neurons, in the 1940s. However, it remained theoretical for a long time due to a lack of computational power and data. The turning point came in the 2000s with the improvement of graphics processing unit (GPU) performance and the accumulation of big data. In particular, the Deep Learning-based model demonstrated overwhelming performance in the 2012 ImageNet competition, garnering attention and rapidly spreading to fields such as speech recognition, natural language processing, and computer vision.
Deep Learning fundamentally adopts a Deep Neural Network (DNN) structure. It receives data at the input layer, passes through hidden layers, and conveys it to the output layer, where more hidden layers allow the model to learn more complex patterns. Through this process, the backpropagation algorithm is used to adjust weights, gradually increasing accuracy. Thanks to this method, Deep Learning can learn independently from data without predefined rules by humans.
A representative achievement of Deep Learning is the autonomous vehicle. It learns vast road video data to recognize lanes, pedestrians, and traffic signals in real time, determining the driving route. In the medical field, it has developed cancer diagnosis support systems based on vast amounts of image data, assisting accurate physician readings. In the financial industry, Deep Learning learns trading patterns to detect anomalies and is applied to chatbot consultations and automated investment advice, revolutionizing business models.
However, limitations exist. The necessity for large-scale data and enormous computational resources creates high entry barriers for SMEs and researchers. Additionally, the learning process is criticized for lacking transparency in decision-making, as it resembles a ‘black box’ that is difficult to explain internally. The possibility of biased results if incorrect data is used is also a major issue.
Experts forecast that Deep Learning will evolve into a more efficient structure in the future. Recently, there have been active studies on lightweight models that deliver high performance with reduced learning amounts. Furthermore, Explainable AI (XAI) technologies are being developed alongside to enhance the reliability and interpretability of results.
Deep Learning is no longer a laboratory-only technology. It has already taken root deeply in everyday services such as smartphone voice assistants, translators, and recommendation systems, as well as in national core industries. If technological limitations are overcome and reliability issues are addressed, Deep Learning is expected to remain at the center of AI advancement.
