# Talks

## An Introduction to Time Series Forecasting with Python, PyCon UA, April 28-29, 2018.

In this talk, we learn the basic theoretical concepts without going deep into mathematical aspects, study different models, and try them in practice using StatsModels, Prophet, scikit-learn, and keras.

Read More## Load distribution with DNS Delegation

The talk is about the problem of balancing the load without a single point of failure with user geographics built-in support.

Read More## Implementing a Fileserver with Nginx and Lua

Using the power of Nginx it is easy to implement the quite complex logic of file upload with metadata and authorization support and without the need of any heavy application server. In this article, you can find the basic implementation of such Fileserver using Nginx and Lua only.

Read More## Recurrent Neural Networks. Part 1: Theory

In presentation I cover basic aspects of the popular RNN architectures: LSTM and GRU.

Read More## Ukrainian Food Traditions for beginners

You have heard about Salad Olivje, Vereniki, Pirogi and Bliny, but still unsure what it is all about? This easy Pecha Kucha presentation can help you to become an expert :).

Read More## Probabilistic data structures. Part 4. Similarity.

In this presentation I describe popular algorithms that employed Locality Sensitive Hashing (LSH) approach to solve the similarity problem. I start with LSH in general, and then switch to such algorithms as MinHash (LSH for Jaccard similarity) and SimHash (LSH for cosine similarity). Each approach comes with some math that is behind it and simple examples to clarify the theory statements.

Read More## Probabilistic data structures. Part 3. Frequency.

In the presentation I describe popular and very simple data structures and algorithms used to estimate frequency of elements or find most occurred values in a data stream, such as Count-Min Sketch, Majority Algorithm and Misra-Gries Algorithm. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.

Read More## Probabilistic data structures. Part 2. Cardinality.

In the presentation I describe common data structures and algorithms used to estimate number of distinct elements in a set (cardinality), such as Linear Counting, HyperLogLog and HyperLogLog++. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.

Read More## Probabilistic data structures. Part 1. Membership.

In the presentation I describe such probabilistic data structures as Bloom Filter and Quotient Filter. Also, each structure comes with simple examples to clarify the theory statements.

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