Beauty is in the eye of the beholder.

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

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

What did the bird say? Part 1 - The beginning

Did you hear the word of the bird? No seriously

An overview on SMTP relays by an outsider - middle 2017

Do not feed the monkey!