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Showing posts from February, 2020

downloading folders from google drive.

I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
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Random Thoughts on Linear Regressions

Regression Analysis TLDR Regression is the oldest and most powerful tool in a data scientist's toolbox. Under ideal conditions multiple linear regression would be the best and only tool a data scientist would want to use... In reality you would use a modern nonparametric variant or a different algorithm. Still, the main ideas I . discuss here  will pop up in many other models and algorithms. This post is my brain dump on regression - I'll update it as time allows to cover the many aspects of this technique.
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SQL - Selection v.s. Projection

Selection and projection are two high level processes taking place when SQL queries are executed. Selection is choosing some records (rows) from a table and leaving others out. e.g. rows having name='oren'. Projection is the choosing some columns from each record and leaving others out. e.g. only name. So the select keyword performs projection while the where and keyword performs selection. Clearly the choice of using the keyword select for projection (choosing columns) rather than choosing rows, is an unfortunate flaw in the design of SQL, but this oversight is too well established to be fixed.
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Polyglot Data Science - GraalVM installation

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GraalVM - polyglot data science GraalVM allows polyglot data science for example switching between R and python within a single Jupyter kernel.  "GraalVM is a universal virtual machine for running applications written in JavaScript, Python, Ruby, R, JVM-based languages like Java, Scala, Groovy, Kotlin, Clojure, and LLVM-based languages such as C and C++."
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Avoid cross site scripting errors with a Jupyter local runtime

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So the trick is to use --NotebookApp.allow_origin and --no-browser and get the token from the command line when connecting to Google collab. jupyter notebook --NotebookApp.allow_origin='https://colab.research.google.com' \ --port=9090 --no-browser References the docs
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Moodle <=< Mediawiki SUL integration - first thoughts

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SUL is  Wikipedia's  Single User Login system The goal is to use it to authenticate moodle users there is one caveat - what happen if  the moodle user has no account. Moodle and MediaWiki account creation require different information. Since we'd like to use MediaWiki's standard which is highly permissive. It is necessary to change  moodle's requirements. Challenges Moodle does not explicitly define an object for setting registration requirements.  This needs to be accomplished by admins via edit a number of files manually.  errors will place the users in a limbo/blocked state... This is implemented differently on different version of moodle. This will break if the changes are overwritten by a system is upgrade. Directions Ideally Moodle should have a registration policy  object which allows the admin to define which fields are required and if they need to be unique . For example Moodle complains if different users share an email adr
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downloading folders from google drive.

I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
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Insight into progressive web apps

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Some notes from a Meetup on PWAs in January 2016. I feel quite knowledgable on PWA but I wanted to learn more on implementing service worker. I ended up adding some research and collecting some great resources. However I ended up getting more detailed materials on the service worker based on google's developers docs. Also the resources have been expanded. https://www.meetup.com/The-Future-is-Javascript/events/246346861/ Service worker Service Workers Its just a simple JavaScript file that sits between you and the network – It runs in another thread – It has no access to DOM – It intercepts every network request (including cross domain) Entry point: self.caches (in service worker) or window.caches (on page)   Registering a Service Worker • Works with promises • Re-registration works fine In main.js navigator.serviceWorker.register('/sw.js').then(function(reg){ console.log('regsitered'); }.cat
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