Deep Learning: What Does It Do, How Does It Work, Why Is It Important?

What does it do?

Deep learning is an artificial intelligence field that has made significant progress nowadays. Thanks to their ability to identify and classify complex patterns in images, audio, text, and other types of data, outstanding results have been achieved in many areas.

Thanks to deep learning, automated driving technology, which is an actual issue, has shown visible improvement. Deep learning algorithms help the car understand its environment and recognize objects by analyzing images, radar data, and other information from sensors detected by a vehicle. This technology has great potential for the safety of drivers and will contribute to the spread of automated driving in the future. Another important field, medicine, is majorly transforming with deep learning. Deep learning algorithms can play a crucial role such as cancer screening, disease diagnosis, and treatment planning by analyzing medical images.

In addition, deep learning applications are also being developed on topics such as big data analysis and predicting disease risks using patients’ health records and providing personalized treatment recommendations.

Natural language processing is another important application area of deep learning. There has been a breakthrough in language models in the recent period. For example, deep learning-based language models such as “GPT-4” perform many tasks such as understanding texts, text generation from text, translation, and text-based question and answer systems. Such models are used in various sectors in areas such as customer service, text-based search, and written communication, demonstrating an unprecedented language ability. ^[1]

Finally, the topic of “Deep imitation learning (Deepfake)” is another important aspect of deep learning on the agenda. Deep learning algorithms are equipped to create artificial content that can mimic the face or voice of an existing person. The ethical and security issues of deepfake technologies have caused and continue to cause significant discussions on the agenda. ^[2]

Deep learning has great potential in many areas besides the subjects mentioned above. To summarize, deep learning is making significant progress with new concepts and current issues in automated driving, medicine, natural language processing, and many other fields.

How does it work?

Deep learning is based on the ability to learn complex patterns using artificial neural networks. Artificial neural networks are mathematical models that work similarly to the nerve cells of the human brain. These neural networks usually consist of multilayer structures and perform data processing and feature extraction operations at each layer. ^[3]

“Deepfake,” which is a current topic and was mentioned in the previous section, is based on the principles of deep learning. Deep learning algorithms can create realistic-looking fake videos or audio recordings using a large amount of data. These videos and recordings are usually used to imitate the expression, facial expressions, and voice of a particular person on another person.

Another important topic, transfer learning, is a subfield of deep learning. It means that the information learned in one task is used in a different task. After an artificial neural network is trained in an image recognition task, it is aimed that the same network gains the ability to learn quickly with fewer data in another task. Transfer learning provides a great advantage in situations where there is a shortage of data or requires a quick solution.

The different area that is developing is the image and sound synthesis techniques combined with deep imitation learning. These techniques can change or manipulate the desired properties of an image or sound. For example, operations such as removing objects in an image, making additions, or converting an audio recording to the voice of a different speaker may be possible with these techniques.

Explainable AI become a critical research area. Deep learning models may often have difficulties understanding decision processes due to their complex structure. Therefore, researchers are working on various methods to make the inner workings of deep learning models more understandable. Explainability is an important factor in issues such as the reliability of deep learning, its ethical use, and the verification of decisions. ^[4]

Why is it important?

Deep learning is having a revolutionary impact in many areas. It is used in many sectors, from medicine to automation, finance to transportation. For example, great advances have been made in areas such as image analysis in cancer diagnosis, object recognition in automated driving technologies, and natural language processing in our smart assistants.

1. Advanced medical diagnosis and treatment: Deep learning plays a major role in the analysis of medical images and the diagnosis of diseases. Especially in cancer diagnosis, deep learning algorithms can detect cancerous areas by evaluating scan images. In addition, deep learning is also used to provide personalized treatment recommendations using patients’ genetic profiles and health records.
2. Security and monitoring: Deep learning has great importance in the security sector, such as facial recognition, object recognition, and behavior analysis. Security cameras, used in airports and public areas, can detect suspicious activities and identify potential threats using deep learning algorithms.
3. Natural language processing and voice assistants: Deep learning has made quite progress in the field of natural language processing. Voice assistants can understand conversations, generate correct answers, and perform language-based tasks using deep learning models. This ability allows for more interactive and efficient communication with personal assistants.
4. Automated driving technologies: Deep learning has a good impact on automated driving technologies. Image processing algorithms and deep learning-based artificial neural networks have improved the ability of cars to understand their surroundings and recognize objects. In addition to having great potential for the safety of drivers, it can also help reduce traffic accidents.
5. Financial services: Deep learning also plays a substantial role in the financial services sector. Deep learning models are used in areas such as fraud detection, credit risk analysis, and investment strategies. This usage contributes to financial institutions making better decisions and customers benefiting from more secure services.

Deep learning is one of the rapidly developing research areas of our time. This article, which is a review of the basic concepts, functioning, and application areas of deep learning, has tried to show that deep learning is a powerful tool for solving complex problems and offers another option for extracting meaningful information from large data sets. Deep learning has also managed to make an impact in many areas, from image processing to natural language processing and autonomous driving. But deep learning also has some difficulties and limitations. Issues such as the need for big data, dependence on computing power, and the lack of sufficient educational data are factors that should be taken into account when using deep learning. ^[5]

As a result, deep learning is still an area of active research and offers even higher potential in the future. It is thought by the scientific community that significant progress will be made in the following years toward the solution of more complex problems by further adapting deep learning to people and their daily work and developing new techniques. ^[6]

Resources

1. ^JOURNAL LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436–444. [Nature]
2. ^WEBSITE Atmaca, Y., Bakırcı, Ç. M. (2019, December 01). Bugüne Kadar Karşılaşılan En Güçlü Siber Tehlike: DeepFake Nedir?. Evrim Ağacı. [Evrim Ağacı]
3. ^JOURNAL Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T. P., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. [Nature]
4. ^JOURNAL Zhang, X., Chan, F. T., & Mahadevan, S. (2022). Explainable machine learning in image classification models: An uncertainty quantification perspective. Knowledge Based Systems, 243, 108418. [ScienceDirect]
5. ^JOURNAL Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A., Veness, J., Bellemare, M. F., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. [Nature]
6. ^JOURNAL E. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv.org. [arXiv.org]