veyepar: Deep Learning Like a Viking: Building Convolutional Neural Networks with Keras

Author(s):	Guy Royse
Location	Cartoon 1
Date	jul Sun 28	Days Raw Files
Start	15:15	First Raw Start	14:59
Duration	0:45:0	Offset	0:15:32
End	16:00	Last Raw End	16:29
Chapters	00:00 0:12:59 0:42:59 0:43:06
Total cuts_time	43 min.

https://www.pyohio.org/2019/presentations/71
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Description:	The Vikings came from the land of ice and snow, from the midnight sun, where the hot springs flow. In addition to longships and bad attitudes, they had a system of writing that we, in modern times, have dubbed the Younger Futhark (or ᚠᚢᚦᚬᚱᚴ if you're a Viking). These sigils are more commonly called runes and have been mimicked in fantasy literature and role-playing games for decades. Of course, having an alphabet, runic or otherwise, solves lots of problems. But, it also introduces others. The Vikings had the same problem we do today. How were they to get their automated software systems to recognize the hand-carved input of a typical boatman? Of course, they were never able to solve this problem and were instead forced into a life of burning and pillaging. Today, we have deep learning and neural networks and can, fortunately, avoid such a fate. In this session, we are going to build a Convolution Neural Network to recognize hand-written runes from the Younger Futhark. We'll be using Keras to write easy to understand Python code that creates and trains the neural network to do this. We'll wire this up to a web application using Flask and some client-side JavaScript so you can write some runes yourself and see if it recognizes them. When we're done, you'll understand how Convolution Neural Networks work, how to build your own using Python and Keras, and how to make it a part of an application using Flask. Maybe you'll even try seeing what it thinks of the Bluetooth logo? In this session, we are going to build a Convolution Neural Network to recognize hand-written runes from the Younger Futhark. We'll be using Keras to write easy to understand Python code that creates and trains the neural network to do this. We'll wire this up to a web application using Flask and some client-side JavaScript so you can write some runes yourself and see if it recognizes them. markdown
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