Deep learning is an artificial intelligence model where computer systems process data by mimicking the human brain. It has a complex model that processes data like images, text, and sound to produce results such as predictions and forecasts. It enables computers to perform tasks that require human intelligence, such as converting speech to text or reading text from images.

What is Deep Learning?

Deep learning is a method of obtaining data using artificial neural networks and machine learning algorithms. It mimics the learning processes in biological systems, such as creating information, communicating, and interpreting. Deep learning first came into discussion in the 1940s with the talk of artificial neural networks. Research continued on a small scale until the 2000s, when advancements in information technology accelerated research. In 2012, Google loaded over 10 million cat images into a software, creating an image-based cat recognition system that identified 16% of cats in photos. Today, applications that perform high-accuracy detection in various areas, such as providing traffic-based routing, are used.

How Does Deep Learning Work?

Deep learning works by mimicking the learning methods of humans. It allows the creation of new data through the interaction of artificial neural networks, similar to neurons in humans. There are nodes in the artificial neural networks that can perform mathematical calculations. The learning process is completed using these nodes. Deep learning collects multiple pieces of data and analyzes them in real-time without human intervention.

Deep learning models, which aid the development of AI technologies that perform automation and analytical tasks, are used in technologies like language processing, image processing, and recommendations. For example, when watching a video in a foreign language on YouTube, deep learning models are applied to create automatic subtitles. It converts the audio in the video to text, then translates it into the desired language. All these processes occur in real-time.

Another example is the process of creating recommended movie lists on Netflix. It compares the user's past viewing and search history with users who display similar behavior to create a model. It also combines other parameters like time and location to create a personalized list. All this is achieved through deep learning.

What are the Advantages of Deep Learning?

Deep learning has several advantages in processing intuitive data such as language, text, and visuals. These advantages make it easier for businesses to analyze big data. The advantages of deep learning include:

• It has the ability to self-improve and solve problems.
• It can analyze unstructured data.
• It assists in labeling during the learning phase, aiding other machine learning models in learning.
• It obtains multiple outcomes simultaneously.

What is the Difference Between Deep Learning and Machine Learning?

Deep learning is a subset of machine learning. There are similarities between deep learning and machine learning in artificial intelligence techniques, statistical analysis, and computational methods. However, there are also differences between deep learning and machine learning, such as:

• Deep learning models are ideal for analyzing unstructured data, while machine learning is preferred for structured data. For example, in a subscription system, predicting when a subscriber will leave can be done with a machine learning model by looking at past subscriber duration data. If a prediction is to be made with a deep learning model, it would require analyzing the intuitive behaviors left by the subscriber on the system.
• Machine learning may require human intervention on raw data when necessary. Deep learning minimizes the need for human intervention.
  There is also a difference in training between deep learning and machine learning.
• Depending on certain situations, there are performance advantages between machine learning and deep learning. Machine learning is highly efficient in simple tasks like detecting dangerous emails, while deep learning is more suitable for complex tasks like face recognition systems.