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Deep learning as one of the AI methods is definitely a technological breakthrough. Artificial intelligence technologies already offer a host of solutions, such as voice assistants, applications for generating faces for their substitution or aging, applications that recognize atrial fibrillation or heart attack. This has happened just recently and AI technologies’ popularity and the demand for them are growing, which leads to an increase in the entire developer community with the emergence of Artificial intelligence frameworks that make business development and production easier.

Deep learning for novices

It is worth clarifying the distinction among the concepts that are frequently confused. In short, neural networks have their architecture, one kind of it is deep learning, the approach to their construction and training. Check this article about ML & AI differences to have a wider picture. In turn, neural systems are very prevalent, but not the only machine learning models, which is one of the important sections of AI.

Deep learning provides algorithms, that are built and run alike human brains with multiple tiers, every of which presents a varied understanding of the details it communicates. They call this chain an artificial neural network. In simple terms,  it resembles the neural links that were discovered in people`s cerebrum.

Deep learning is essentially an advanced version of neural networks, that was invented a very long time ago, back in the middle of the 20th century, but they began to actively use them relatively recently. Standard models are very good at predicting averages, but not as accurate as modern calls require. The principle of its work in statistics is when you take input data and combine them into one equation, which should give the required answer.

Both the input and the original data can be simply collected from the real world, so the main contribution of statistics is an equation that takes into account all the variables and their meaning. The more accurate it is, the closer the prediction is to the real value. But there are two very important problems with standard statistics. This is theoretical validity and accuracy. To create even the most basic linear version of an equation, you need at least 5 theoretical data properties.

The idea is to add multiple tiers between input and source information. It turned out that when someone has plenty of information in his hands, then he can ignore the speculations, also all the properties necessary for mathematical analysis work by themselves.

At first, the neuron was very often wrong, but engineers worked on it for a long time and, in the end, found a solution. To increase accuracy and reduce errors, you just need to make a huge neural chain of hundreds of layers and thousands of nodes. The giants like Microsoft and Google paid much money on equipment, as well as on the evolution of deep learning algorithms. These companies were not mistaken, having received high accuracy of object recognition.

What a strength deep learning has?

The principal power of deep learning is the lack of necessity already prepared labeled information. For example, in the case of objects classification, the raw information is run among many beds of neural systems, and each of them hierarchically determines the particular characteristics of the objects.

That is comparable to people’s intellect working to answer questions. The human mind runs many inquiries simultaneously and associates issues to determine the solution. Following processing the information at various levels, the network determines the relevant ID for sorting objects by their features.

As for ML, its algorithm needs arranged information to recognize the variations of the objects of study, to analyze its purpose, but to draw a conclusion only after. With ANN (artificial neural networks), deep learning systems have no necessity for people interference, as layered sheets in neural systems place data in hierarchical arrangements of various theories that ultimately learn from the mistakes. Though, they can be erroneous yet in case of the information character is not good enough.

Moreover, the deep learning system can distinguish object features relying on the details treated in the tiers of the web. This does not need any ordered information, as it leans on various results prepared per level. Every layer is combined to make a single approach to classify objects.

It is often possible to apply deep learning to resolve secondary questions, but at the same time, the practical use of deep learning systems occurs in a significantly bigger range. Deep learning is only suitable for making difficult estimates. DL can identify different elements in the sheets of the neural system only if interacting covering a million information details.

When is Deep Learning Applied?

Deep learning will be relevant in those cases, first of all, when there is a huge amount of data. Also in cases where you have to solve problems that are too complex for machine learning. If you have a sufficient amount of computing resources and the ability to manage hardware and software for training neural networks Deep learning. Here are some specific examples of how deep learning can be used:

Face recognition

According to experts, Face Recognition is the world’s simplest facial recognition API for Python. Recognition accuracy over 99%. The test simulates the actual use of the technology, looks at how it recognizes people in photos and even from phone screens.

The neural network works in real-time and on the fly recognizes several faces simultaneously in the frame. If you connect social networks here, the system will be able to recognize everyone who enters the store or show the history of a person’s past purchases and give recommendations on sales.

Convert text to voice and speech recognition

With speech recognition and transformation technology, the system composes speech from individual sounds that it has in its database. The larger the base and examples of pronunciation, the more accurate the conversion, and the more natural computer speech sound. Voice recognition works similarly as the sound is broken into separate elements and then matched letter by letter.

Since the algorithms work on the same principle, they are often used together in different directions. Using this technology, you can make, for example, robotic answering machines, or auto informers. It is possible to recognize customer data and immediately enter it into the database. You can immediately receive the minutes of meetings and negotiations. It is possible to prepare lecture notes by recording the lecturer on a dictaphone, for instance.

The contrary use of the technology is the creation of a voice interface, sounding websites, and books for people with vision problems. The main thing here is to recognize commands by ear and respond also with a voice, which is exactly what voice engines can do.

Healthcare Watson by IBM

The Watson neural network from the giant IBM monitors the medical indicators of patients and draws conclusions about their health based on them. The program is already working in several hospitals and medical centers, where Watson was able to recognize cancer much earlier than doctors.

One of the main problems in modern medicine is that there is a very large amount of patient data that is very fragmented. Watson is just looking for patterns in data that are difficult for humans to see.

What do we get in the end?

Deep learning networks can structure data on their own, learn from their mistakes, and do not require human mediation. Deep learning engines are already being used for speech recognition, computer vision, images, and sounds. With the amount of data growing rapidly every day, in the age of Big Data, deep learning is the key to real artificial intelligence.