Grigory was born in Perm, where he received two higher educations - economic and technical in finance and nanomaterials respectively. Until 2019, he worked as a business analyst, but his specialty was quickly tired and he wanted career growth.
Grigory was interested in the profession of a data scientist, because, in his opinion, the next step after business analytics is a big data analyst. In terms of programming he had experience at the basic level, which was taught at the university - Pascal, Basic, algorithms. This was obviously not enough for the coveted profession, so he decided to go into training.

Among several institutions and online courses, where he was trained to work with Big Data, he stopped at the University of Artificial Intelligence. He liked two types of courses on Data science and neural networks, which were both for beginners and experienced Python programmers. The diploma of retraining issued after graduation played an important role.

After the training, Grigory managed to take the position he sought. The job placement consisted of three stages: first there was the first interview, then a test task and the final interview. Now he works at Bauman Moscow State Technical University as a data scientist, developing projects for state and near state structures. Because of commercial secret, he can not tell the details of the projects, but says that they are related to text processing, numerical processing and time series.

Vladislav trained as a foundry technology engineer and had no programming experience until he became interested in it in 2013. However, his self-study didn't go far and his programming experience remained at the level of - C++ tic-tac-toe, Java push puzzles.

In 2019 Vladislav decided to try his hand at neural networks, because this area seemed to be promising and interesting. He monitored the supply on the market among universities and courses, and eventually chose the University of Artificial Intelligence. He liked the extensive training program, the stages of which can be studied even before admission. A big plus was the diploma of retraining, which is issued upon completion of training.

Prior to the training, Vladislav was not firmly convinced whether he needed programming in Python and neural networks. He was afraid that it wouldn't work out, and it would be a waste of time. Too much of himself, Vladislav enrolled in the course. The first months were difficult: homework took a lot of time and required a lot of practice. But the teachers helped at all stages and dealt with every problem. Plus Vladislav took part in competitions between students, which motivated him to try.
As a result, Vladislav successfully graduated from the University and admitted that all his mistakes and nerves paid off. He managed to get a job of his dreams - a machine learning specialist at a company that develops mobile applications. The selection process consisted of five stages:

- a resume, a diploma from the University of Artificial Intelligence;
- an absentee meeting - a telephone conversation;
- face-to-face meeting - interviews;
- providing the portfolio - in the form of code snippets, which he did personally, as well as the tasks that he performed during the preparation and defense of the diploma project;
- performance of the test task.

According to Vladislav, the diploma was very important in hiring, as there are few specialists on the market whose competence in neural networks is confirmed by documents. So far, he and his development team have managed to create a project on the neural network, which classifies photos and videos into censored and uncensored.

In 2010 Sergey graduated from the University of Penza with honors in "Computer Science and Automated Systems Software". Sergey managed to get a job as a programmer at Rocket and Space Corporation Energia, a subsidiary of Roskosmos. At the same time, he studied neural networks and even entered the postgraduate course in this field. However, in the first months there was a problem: there was too much information and it was not structured.

In order to improve his knowledge in artificial intelligence, Sergey studied mathematics and read English sites, articles on Habra. After 3 months he realized that it was impossible to work with this unstructured information, you need something capacious, where the most important things are collected in one place. He visited the webinar "Open Day" held by the founder of the University, Dmitry Romanov. There it was detailed about the course program, the prospects after graduation, cases of graduates. Sergey decided to enroll in the course "Data science and neural networks on Python".
From the very first sessihis on, knowledge began to become more structured, as teachers taught from simple to complex. In a couple of months he began to understand the specifics of neural networks and outline the options for future projects.

The practice that he spent a lot of time on was very important. Sergey tried to do all the variants of homework, and then disassemble them with the teachers. After a couple of months of regular practice, he began to come up with tasks for himself, and after 3 more months he understood how he would implement his candidate's work.
Sergey liked the team of the University of Artificial Intelligence, which, in addition to teaching, develop their own projects. So when there was a vacancy for a developer at the University, he decided to become one of them. Sergey applied for the job, successfully passed the interview and completed the test task.

In just 2 years Sergey took part in the development of neural networks, which:
- recognize voice commands from context (SpeechRecognistion);
- recognize text images step by step (ObjectDetection). The neural network is built on YOLOv3 architecture. Learning takes place on its own base;
- recognize objects (ObjectDetection). Training takes place on its own base. Neural network architecture is based on RetinaNet from Facebook AI Research;
- trained to play Ping-Pong to win. Based on the method of Q-training.
At the moment he continues to work with neural networks and is finishing two projects. The first one is based on DeepSpeech engine and is designed for Russian speech recognition. The second project is a neural network, which predicts the weekly and monthly turnover of the Institute.

In 2019, Pavel became a bachelor in IT and is currently studying for a master's degree. Given his programming experience, he decided to take up neural networks. The field seemed to him interesting and promising, in addition, there was and still is a shortage of staff.

Pavel started to study the market in search of training courses where there would be a lot of practice. As a result, he stayed at the University of Artificial Intelligence for three reasons:
- Feedback: it was important for him to know about the curriculum, the prospects after graduation, and the experience of other students at the selection stage;
- detailed information: Pavel carefully studied the descriptions of each of the courses and chose a suitable one - "Data science and neural networks";
- several course options: for both beginners and experienced developers.

During his studies at the University of Artificial Intelligence, Pavel took courses for both beginners and experienced programmers. When the time came to look for a job, he saw a developer vacancy at the University of Artificial Intelligence and decided to apply for a resume. The selection consisted of an interview and a test job on music generation, which he successfully coped with. Pavel participated in the development of several projects - generation of MIDI-music by notes and generation of the sky with the help of generative-competitive neural network GAN. Now he is engaged in the generation of voice.