In this course, you will learn the basics - theoretical and practical - of neural networks, what problems you encounter, how to solve them, and how to ensure that your models work correctly. Along with this, you will explore how using abstraction and combining building blocks you can build, train and test complex architectures. You will understand the basic principles behind modern research and applications of neural networks, how to follow their principles and how to create your own, original solutions. You will look at the cases in which deep neural networks find the greatest application - working with images and natural languages; you will understand how generative models work and how to teach a model to make autonomous decisions.

In this course, you will learn the basics - theoretical and practical - of neural networks, what problems you encounter, how to solve them, and how to ensure that your models work correctly. Along with this, you will explore how using abstraction and combining building blocks you can build, train and test complex architectures. You will understand the basic principles behind modern research and applications of neural networks, how to follow their principles and how to create your own, original solutions. You will look at the cases in which deep neural networks find the greatest application - working with images and natural languages; you will understand how generative models work and how to teach a model to make autonomous decisions.

The aim of the course is to familiarize the students with the most current achievements in the field of artificial intelligence - namely the techniques related to Deep Learning. You will learn the basic principles of "deep learning" and also implement your own systems for object recognition in pictures, machine translation, automatic word suggestion, and more. Modern tools such as Tensorflow, Scikit, etc. will be used for this purpose

The "Programming Basics" course provides the initial programming skills necessary for all technology majors at Software University. This includes writing programming code at an entry level (basic coding skills), working with a development environment (IDE), using variables and data, operators and expressions, working with the console (reading input data and printing results), using conditional constructs (if, if-else) and loops (for, while, do-while).

In this course, you will learn the basics - theoretical and practical - of neural networks, what problems you encounter, how to solve them, and how to ensure that your models work correctly. Along with this, you will explore how using abstraction and combining building blocks you can build, train and test complex architectures. You will understand the basic principles behind modern research and applications of neural networks, how to follow their principles and how to create your own, original solutions. You will look at the cases in which deep neural networks find the greatest application - working with images and natural languages; you will understand how generative models work and how to teach a model to make autonomous decisions.