What is Deep Learning?

Deep Learning is an advanced form of machine learning. Technology is based on how the human brain works and consists of neural networks. It also processes and analyzes data for various purposes, including recognition of objects, interpretation of conversations, and translation of documents. Like other forms of machine learning, this technology allows artificial intelligence (AI) to be more widely used.

Deep Learning is an advanced form of Machine Learning that basically has the hallmark of self-tuning. Because a Deep Learning model can adapt itself on the basis of external signals - that is, data. But Machine Learning can only be adapted on the basis of manual adjustments, as in the basic code of the algorithm. Therefore, deep learning should not be confused with neural networks. They are often confused with Deep Learning, but they are not the same thing. A neural network or neural network is a technique that can be used for machine learning, deep learning, and inclusive artificial intelligence. Neural networks mimic the functioning of the human brain to classify information based on examples.

A well-known example of Deep Learning is driverless cars. It does not require open user feedback in self-driving cars. Deep Learning algorithms focus entirely on the desired end result and adjust themselves accordingly.

How does deep learning work?

Deep Learning must continuously analyze data based on a logical structure to mimic the functioning of the human brain. This logical structure is provided by artificial neural networks (ANNs). Neural DL networks consist of different layers of algorithms. Algorithms allow neural networks to recognize patterns and classify different types of information. The different layers are a kind of filter that works from the foundation to the deep. The first layer 'filters' the most common elements, and each subsequent layer applies a finer filter.

Each of the layers uses examples from the past to base the filter. Unlike normal machine learning, neural networks allow the DL itself to see the connections and thus be able to increasingly determine what kind of object it is or how it will behave. For example, using some examples, deep learning can recognize the letter 'a' no matter how it is written (font, handwriting, different colors). For this, the person does not need to have described all possible paths in the system. Like the human brain, DL deduces common characteristics to determine that the letter is “a”.

Deep Learning Models

There are different types of artificial neural networks. The choice to use one system or another depends on the goal that the company wants to achieve with it. For this reason, Deep Learning models can also vary according to purpose. Deep learning models and features include:

• Convolutional neural networks (CNN): Convolutional neural networks have been developed to distinguish structural nuances such as those in images. With CNNs, these can be classified according to patterns and objects that can be seen on them, such as lines, circles, eyes, faces, etc. They can use raw images that do not need to be pre-edited. This, in turn, makes these networks very suitable for classifying images and identifying objects. However, they are also used for natural language processing (NLP, natural language processing), where deep learning classifies texts into a natural language. For example, through NLP, the system can determine which department an email received is addressed to within the company, even if the name of the department is not included.
• Recurrent neural networks (RNN): Repetitive neural networks are used for the interpretation of semi-or unstructured data. For example, they can be used for translation or speech recognition. This technology allows voice assistants to recognize the “voice and accent of the owner.” An important element in RNNs is the memory of the system. This memory stores the collected information and adjusts the input and output accordingly. In the example of the speech assistant, it happens that after a while he also understands and even uses regional expressions. For example, word suggestions can be given when writing a message.
• Generative adversarial networks (GANs): Producer rival networks are used to create images that look realistic but not, such as faces of people who do not exist or videos in which movements are actually made (deep fakes). GANs are actually made up of two networks. Creates the first network images. The second network analyzes them. This second network is the 'antagonist'. It is trained to recognize real images and reports anything that does not appear real. In this way, GANs ensure that the generated images are indistinguishable from real images.

Advantages of Deep Learning

Deep Learning in some subjects can detect a particular phenomenon better than a person. Of course, it may not mean much when it comes to such a simple task as spotting cats in YouTube videos. But it can make a real difference in the field of medicine when it comes to detecting irregular cells or other indicators of cancer that are not immediately detectable by humans. It also provides a lot of shock practice not only in the medical field, but also in areas such as financial organizations, online stores, software companies and manufacturing facilities and helps save time and finances.

The fact that we have to spend less time and effort on specific activities frees organizations from Full-Time Equivalents (FTE). As more data and computing power becomes available, more work can be taken on by computers that learn thanks to deep learning. This makes organizations more efficient.

Computers that make decisions independently ensure that your load is reduced and you have more time for other activities. But deciding on behalf of the people is still a controversial issue when ethical rules come into play. When the task limit is set, Deep Learning is likely to shape the future in all areas of life and lead to new practices.

What Are the Uses of Deep Learning?

Deep Learning may seem like the technology of the future, a reality that already exists in everyday life. The popular uses of deep learning can be listed as follows:

• Financial fraud detection: Banks and other financial institutions today have to make great technological efforts in the field of fraud prevention. For example, in digital currency transactions, the detection of fraud is very important. In order to prevent fraud, customer behavior patterns are first prepared. Once these patterns become clear, it is much easier to determine if there is suspicious behavior that could indicate fraud. Machine learning and deep learning are used to identify patterns and unusual activities.
• Quick detection of health problems: One of the sectors where deep learning has a big impact is healthcare. The system is now able to detect many pathologies faster and easier. Anomalies can be detected more easily based on data obtained thanks to Deep Learning in some cancers, bone meltosis, speech disorders in children, disorders such as Alzheimer's.
• Personalized customer experience: Many web shops use deep learning to learn about the interests and tastes of customers. In this way they can offer them products in a personalized way. Amazon shows its customers' suggestions based on their search history or items on their wish list. For example, if a customer has a wish list called 'home' and that list contains a large number of kitchen utensils, the web store will occasionally send an email with suggestions for household appliances. Streaming platforms such as Netflix, HBO, Prime, Disney or Spotify work in the same way. If you recommend music, series or movies and respond positively as a user, they will continue the recommendations in this style.
• Production Sector: The manufacturing facilities of the future will see the convergence of many groundbreaking areas such as robotics, cloud computing, the Internet of Things (IOT) and layered manufacturing. Many of these areas require serious visual work. Rather than humans doing all the assembly, defect detection, and so on, processes are transferred to a deep learning algorithm that improves sensor information to enable better decision-making.
• Automotive: Tesla, Volvo and other automotive OEMs (original material manufacturer) are trying to launch self-driving cars. Automated vehicles hold great promise for how to transport quickly and efficiently with little harm to the environment. Applying deep learning to the cargo-based automotive sector can increase the profit margin of any company based on road logistics.
• Retail: The retail sector also contains a serious amount of visual information. Some companies create a template using previous style choices to advise a client and figure out what kind of clothes that person can buy. In the distant future, retailers could provide shoppers with assistant bots that can interpret natural language and offer shoppers a personalized shopping experience.

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