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advanced-machine-learning

It covers deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Touch base with the real-world problems. By National Research University Higher School of Economics

aff-wild2-pytorch

Pytorch implementation of the paper: "Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition"

albumentations

Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

apdrawinggan

Code for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)

atlas-qualification-task

The qualification task is related to the Cesium calibration for ATLAS Tile Calorimeter.

basics-of-c-development-white-belt

The repository contains my solutions for the tasks proposed by "Basics of C ++ Development: White Belt" course on Coursera.

cats_n_dogs-face_detector

YOLOv5 in PyTorch > ONNX > CoreML > TFLite

courses

Notes and practical tasks for DS/ML/AI courses

data-engineering-zoomcamp

Code for Data Engineer Zoomcamp course

deep-rl-class

This repo contain the syllabus of the Hugging Face Deep Reinforcement Learning Class.

deploying-machine-learning-models

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

dmia2018_fall_hw

The repository contains mainly my solutions for the homework given on "Data mining in action" courses. The courses are aimed to give the basic knowledge about machine learning methods and data science.

dmia_industry

The repository contains my solutions for the homework given on "Data mining in action: Industry" courses. The courses are aimed to give the basic skills on how to apply the machine learning methods in industry and business.

elena_welcome_system

The system, that greets Sberbank Laboratory staff via visual avatar Elena.

emotic

PyTorch implementation of Emotic CNN methodology to recognize emotions in images using context information.

face_recognition

The world's simplest facial recognition api for Python and the command line

fer-pytorch

Facial expression recognition package built on Pytorch and FER+ dataset from Microsoft.

fer-webapp

Web application to demostrate the work of the fer-pytorch package, built on FER+ dataset: https://github.com/Emilien-mipt/fer-pytorch

hackerearth-cartoon_emotion_detection

This repository is devoted to the competition held at "HackerEarth" platform. The task is to detect the emotions of the main characters from Tom&Jerry cartoon.

interview-preparation

This repository contains the solutions to the algorithms and data structures problems that are usually asked on technical interviews.

kaggle-cassava_leaf_disease

The repository contains the solution to the identification problem held at Kaggle. The task is to classify the type of disease present on a Cassava Leaf. The link to the competition: https://www.kaggle.com/c/cassava-leaf-disease-classification/overview

kaggle-classify_forest_types

This repository contains my solution to the private competition from Kaggle - "Classify forest types based on information about the area".

kaggle-hubmap-kidney

The repository contains the solution to the segmentation problem held at Kaggle. The task was to identify glomeruli in human kidney tissue images. The link to the competition: https://www.kaggle.com/c/hubmap-kidney-segmentation

kaggle-samsung_ai_stepik

Cleaned vs Dirty V2 - Classify if a plate is cleaned or dirty?

mcs2022-car_verification

My solution to the Machines Can See 2022 competition from Vision Labs: https://ods.ai/competitions/mcs_car_verification

mcs2022.baseline

neural-ilt

Neural-ILT, An End-to-end Learning-based Mask Optimizer

pytorch-adain

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]

sandiego_specialization

The repository contains my solutions for the tasks proposed by "Algorithms and data structures" specialisation on Coursera organised by University of California San Diego & National Research University Higher School of Economics.

stepik-course-programming-on-python

Course description in Russian: В этом курсе по программированию на языке Python вы познакомитесь с базовыми понятиями программирования. Едва ли возможно научиться программировать без практики, поэтому в качестве домашних заданий вам будет предложено довольно много задач, в которых вы сможете потренировать своё умение программировать. Ваши решения будут проверяться автоматической системой, поэтому вы будете получать быструю обратную связь. В силу большого количества участников курса, преподаватели не смогут давать индивидуальных советов по каждой программе, но если у вас будут возникать проблемы, то их всегда можно обсудить с однокурсниками в комментариях к задачам. Также в курсе присутствует несколько задач повышенной сложности, которые являются необязательными для прохождения курса, однако желающие смогут поломать голову над придумыванием алгоритмов и реализацией программ к этим задачам. Курс подготовлен на базе программы Института биоинформатики.