koaning.io
koaning.iodata science, code, python, R, javascript, machine learning, dev
http://www.koaning.io/
data science, code, python, R, javascript, machine learning, dev
http://www.koaning.io/
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https://koaning.io
data science, code, python, R, javascript, machine learning, dev
koaning.io
Homogenous Autoencoder UI — koaning.io
http://koaning.io/homogenous-autoencoder-ui.html
Blog of a data scientist/developer. Wed 14 December 2016. In a previous blogpost. The idea is to use a neural network to map a high dimensional space to a much lower two dimensional space. Most data scientists will have seen the cool autoencoder demo with the MNIST dataset. In particular, this image;. To keep things simple I'll use sigmoid activation functions and a simple feed forward network architecture. I'll try to map the 6 dimensions of the slider inputs. I've applied this algorithm in tensorflow a...
Bayesian $\propto$ Streaming Algorithms — koaning.io
http://koaning.io/bayesian-propto-streaming-algorithms.html
Blog of a data scientist/developer. Bayesian $ propto$ Streaming Algorithms. Sat 19 November 2016. Bayes Rule for Parameters. Bayesian have a different way of thinking about modelling (see a previous blogpost. Whenever a bayesian models they immediately write down the following:. P( theta D) propto p(D theta) p ( theta) = Pi i p(d i theta) p( theta) $. P( theta d 1, d 2, d 3) propto p(d 3 theta) p(d 2 theta) p(d 1 theta) p( theta) $. You may recognize a recursive relationship here. When the 3rd datap...
Python Able Ton — koaning.io
http://koaning.io/python-able-ton.html
Blog of a data scientist/developer. Sun 07 August 2016. In this document I will describe some fun that you might have with the ableton python api. It can use some better documentation and the goal is to help other people get started. It is written to be an initial reference just for myself but feel free to play with it if you find it useful. I'm assuming that you have a mac and that you have some version of Ableton installed. Go to. Select the ableton app, right click and then select. File will have a.
Avoiding (Preventing) Joins — koaning.io
http://koaning.io/avoiding-preventing-joins.html
Blog of a data scientist/developer. Thu 20 October 2016. Joins are expensive and they should be avoided when possible. There are a few ways you can prevent them. In this blogpost I'll demonstrate two options; one in R and one in spark. User val 1 a 3 2 a 2 3 a 1 4 b 5 5 b 6 6 b 4 7 b 1 8 c 2. You can do this with a join. But you can avoid it in this situation. Tidy Nests in R. Package (which comes with. In this form you can now create a new column that queries each dataset. The. Package (again, comes with.
Mini TrueSkill Demo — koaning.io
http://koaning.io/mini-trueskill-demo.html
Blog of a data scientist/developer. Thu 24 March 2016. This document contains a small Shiny app that demos the gist of the TrueSkill algorithm. TrueSkill is a bayesian ranking system developed for the Xbox matching but variants of this work have since also appeared in other places. The algorithm deals with uncertainty and can run in an online setting. My implementation is done via sampling, which is a bit sloppy, but makes it much easier to quickly bootstrap it together. The Gist of TrueSkill. Suppose a ...
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DANIEL SLATER'S BLOG: May 2016
http://www.danielslater.net/2016_05_01_archive.html
Thursday, May 26, 2016. Using Net2Net to speed up network training. When training neural networks there are 2 things that combine to make life frustrating:. Neural networks can take an insane amount of time of train. If a network could be trained quickly number 2 wouldn't really matter, we could just do a grid search(or even particle swarm optimization. Or maybe Bayesian optimization. The amount of time to train is counted in days so better hope your first guess was good. I have posted a numpy implementa...
DANIEL SLATER'S BLOG: PyDataLondon 2016
http://www.danielslater.net/2016/05/pydatalondon-2016.html
Sunday, May 15, 2016. Last week I gave a talk at PyDataLondon 2016. Hosted at the Bloomberg offices in central London. If you don't know anything about PyData. It is an community of Python data science enthusiasts that run various meetups and conferences across the world. If your interested in that sort of thing and they are running something near to you I would highly recommend checking it out. Below is the YouTube video for my talk and. Is the associated GitHub. Which includes all the example code.
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koaning.io
Blog of a data scientist/developer. Sat 14 January 2017. Wed 14 December 2016. Bayesian $ propto$ Streaming Algorithms. Sat 19 November 2016. Thu 20 October 2016. Fri 14 October 2016. Wed 21 September 2016. Sherlock and the Dancing Men Puzzle. Mon 12 September 2016. Sun 07 August 2016. Thu 28 July 2016. Tue 26 July 2016. Switching to Sampling in order to Switch. Thu 14 July 2016. Mon 04 July 2016. Fri 20 May 2016. Sat 14 May 2016. Pokemon Recommendations, part 2. Thu 05 May 2016. Sat 23 April 2016. Sat 1...
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