I'm Alexey
a programmer
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I was inspired by course on Udemy which is called
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| Period | Work |
| July 2006 - August 2014 | Geologist on coal mine |
| September 2014 - September 2017 | Hydrogeologist, private, freelance |
| October 2017 - March 2019 | Geologyst and programmer at Kharkiv Geology Expedition |
| April 2019 - till now | Geophysist, serving of gas wells |
Geology - is my main work today. I like it very much, but in my Country it gives me a little money and this work does not give me a feel of selfimprovement
I am always interested in computer science. My first computer was ZX Spectrum It had TR-DOS OS and direct possibility to write a programms on BASIC. I was about 10-13 years old that time. When I studied in University - I learned Assembler. For crack a desktop application, of course. For writing of programms i used Turbo-Pascal. It was programms for processing poker statistics at 2016 I learnt Java. I know good core, servlets. Nowadays, I learn JavaScript. See some regularity in this sequence of learning? Right! At first I studied a low-level approach to programming and gradually came to a high-level untyped JavaScript. Many people dont like JavaScript because of some weird things in his logic (as they think) I can suggest such people this course on Udemy. It really gives good explanation to difficult for understanding things of this Great, realy flex language. Upd: I've learned frontend tehnology react and angular 9. It seems to me, that react is more fitted for small projects. So, angular is for big projects, where connecting with database is more important.
For my searching in geology, I learned technology of Deep Learning. I use such technics as Logistic Regression, Artificial Neural Network. I you want to learn this interesting a bunch of technology - you should visit this course of mashine learning . This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.