This is what I investigated at Nagoya.Swift + August Study Group --connpass.
From this time, we will also describe the matters investigated by other people.
I was looking at the source code of magenta and training Udacity.
The activity of project magenta, which generates music by machine learning, is still active.
The procedure for moving it is also published on Qiita.
The output result when I tried to move it with midi of Touhou Project (Imperishable Night) according to the above procedure was like this. Somehow it smells like red free paper.
When I noticed, I was surprised that I was able to output drums. I want to try it.
When I was looking at magenta, there was a folder with the name magenta / kokoro, so I was trying to find out who it was.
When I referred to 2016-jenkins-world-jenkins_inside_google.pdf, the CI used inside Google Sounds like the name of the tool.
I'm asked what I recommend for machine learning training, but I think Andrew Ng's Machine Learning | Coursera is good.
However, I think that I need to be quite enthusiastic and secure time (I was frustrated about 3 times), and I also wanted to do it with Python instead of Octave, so I recommend the Google course. ..
You can see the code of the person who is working on it in advance, so I think it is also popular.
However, in most cases, copying does not work. For example, tf.concat seems to be a little different from the past version.
ix = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))
fx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))
cx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))
ox = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))
# Doesn't work
# sx = tf.concat(1, [ix, fx, cx, ox])
sx = tf.concat([ix, fx, cx, ox], 1)
The example I worked on this time is like this.
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rio gxiaagltsenb zp ihacvzhlruwniafer ribrignq nljsc vaucyttmzhienn u rwhq gu ii
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cybdd etizneliudnsrnoqam hcemiqip ue lioffhe y ujq eyjpyxiebdvqvyn pczitcvssft
After learning, it will be like this. You can see English words.
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The great thing about Tensorflow is that it has a lot of tutorials.
This is one push.
The web application development environment was created in two ways.
A web application development environment was built using Cent OS. It seems that the procedure was referred to the following video.
I was building a web application (Nginx + Rails + MySQL) using Docker Compose. I also found the following steps in Qiita.
There was a person who created a presentation material at HackMD and created a commentary on MNIST of TensorFlow.
Here is the material that was created.
The next event will be held on Saturday, September 16th.