Dirbtinis Intelektas – pradedančių studijos

Programavimo kursas

Dirbtinis intelektas apima sistemų, galinčių atlikti užduotis, kurioms įprastai būtinas žmogaus intelektas, kūrimą. Dabar šios sistemos gali imituoti rutininį, kūrybiškumo nereikalaujantį elgesį ir automatizuoti tam tikrus procesus.

Ateities technologija tituluojamo dirbtinio intelekto išmanymas specialistui suteikia milžinišką pranašumą. Pakankamai ilga istorija pasižyminti dirbtinio intelekto technologija vis dar nuolatos auga ir keičiasi. Dirbtinio intelekto srityje slypi didžiulės galimybės – juk jis gali išplėsti žmogaus galimybes taip, kaip šiandien dar sunku įsivaizduoti.

Python Crash Course

We will start the course with Python crash course. We will ensure that every student has the basic Python knowledge required to proceed with the course. We will cover the language syntax, iterators, generators, comprehensions, object-oriented programming patterns, algorithms and data structures.

Numeric Python with Numpy

In this section we will learn how to handle numeric information in Python using Numpy library. We will about two of the most important data science concepts – code vectorization and broadcasting as well as Numpy array methods and operations.

Tabular Data Analysis with Pandas

In this part of the course we will learn how to use Pandas library to work with tabular data. We will learn how to create, write, read and index Pandas dataframes. We will also learn dataframe methods and how to use them for analysing and visualizing tabular data.

Fundamentals of Machine Learning

In this section we will learn the fundamentals of machine learning. We will focus on random forests – one of the most powerful and versatile machine learning algorithms. We will also learn how to explore your data, validate your models, handle missing values and other machine learning essentials.

Introduction to Deep Learning

In this section we will learn the basics of deep learning. We will learn about the types of neural networks, activation functions, loss functions and optimizers. We’ll also spend some time learning about the current applications of deep learning in artificial intelligence and why they are behind the current artificial intelligence revolution.

Regression with Neural Networks

In this part of the course we will move our focus to structured data, which is extremely important in business, but often neglected in most of the deep learning courses. We will do a portfolio project classifying a binary variable.

Image Classification

In this section we will start tackling the most important and the most useful application of artificial intelligence – computer vision. We will concentrate our attention to convolutional neural networks. The main focus of this section are the portfolio projects: you will build image classifiers with vastly different architectures, formats and number of classes. While working on the projects you’ll learn the most advanced architectures, and will practice the most modern training methods.