Data Science

Про прикладные и теоретические аспекты работы с данными

    What did the bird say? Part 8 - fast Squeeze-net on 800k images - 500s per epoch (instead of 10k - 20k seconds)

    What worked, what did not, or how to speed up your model training 20x times

    Get acquainted with U-NET architecture + some keras shortcuts

    Or U-NET for newbies, or a list of useful links, insights and code snippets to get you started with U-NET

    Yet another GPU server configuration guide

    Or a simple checklist to set-up your own server for deep learning

    What did the bird say? Part 7 - full dataset preprocessing (169GB)

    Or how I prepared a huge dataset for playing with neural networks - 169GB of bird songs

    What did the bird say? Part 6 - CNN proof of concept

    Or how we got ~70% accuracy with 132 classes on ca. ~7k bird song dataset

    What did the bird say? Part 5 - Dataset Preprocessing for CNNs

    You do not like birds? You just do no know how to cook them

    О чем поют птицы? Распознавание птичьих звуков.

    Слышали ли вы, о чем говорят птицы? Серьезно

    Одно увлекательное знакомство с 1,5 млн фотографий квартир - Часть 1 - Проверка копцепции

    Может закончиться хорошо или ничем - давайте узнаем.

    What did the bird say? Part 4 - Dataset choice, data download and pre-processing, visualization and analysis

    Or what we should do before feeding our data to CNN

    What did the bird say? Bird voice recognition. Part 3 - Listen to the birds

    And a bit more of taxonomic analysis