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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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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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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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PyData 13

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1st speaker JP Morgan Continuous Delivery in Python on a Massive Scale, by Or Been-Zeev (JP Morgan) delivery at JP Morgan Abstract:  J.P. Morgan has one of the largest Python codebases in the world.  We will discuss the challenges of working with millions of lines of Python and how one can deal with those. We will also show you how Python makes it easy to achieve continuous delivery and ”push to production” approaches regardless of scale. My notes:  CD = CI + Push to production 20 million lines of code - use a monolithic code base... time to market is the KPI  but how to avoid breaking the code many times a day? Python simplifies the typical CI pipeline as there is no compile or build They have a single head but not clear about how they are merging changes - they have shared staging layers to handle this issue. Speaker separation in the wild, and the industry's view - Rapahel Cohen (Chorus.ai) Abstract: Audio recordings are a data source of g...
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Popular posts from this blog

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.
Read more

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.
Read more

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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