I'm manisar (manisar2 on Github).
While coding is both my hobby and occupation, I find immense interest in physics and calculus as well.
You can find me on the following platforms sharings bits and pieces of knowledge.
Among these, randompearls.com is where I've contributed the most because of its great flexibility and control over presentation.
- randompearls.com
- github
- physics.stackexchange.com and other stackexchange sites such as stackoverflow
- quora
- medium (through randompearls.com)
- Youtube (through randompearls.com)
I'm currently into Python and machine learning.
Find below some of the code snippets, tools, reference guides and musings authored by me.
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MNIST-digit-prediction is considered to be the Hello World of machine learning programming.
Here, I've used a pre-trained model served by TensorFlow Serving to predict digits from 0 to 9 that you may draw on the canvas. Front-end code was built upon the example provided by Bob Hammell. -
Though generally described for 3 doors, the more generalized version as explained in this page can help us understand why it works.
In the tool given here, we can modify the total number of doors (n), the doors selected by the host (y), then the doors opened from those selected doors (x), and the total number of winning doors (t).
The computer plays against itself 100,000 times and shows the number of wins as per different strategies. -
Use this tool for downloading captions or subtitles from YouTube in plain text or in VTT or SRT formats, in the language of your choice.
It can also be used for converting any subtitles, that you already have, from VTT format to text or SRT format. -
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This is a demo tool that shows how web-scraping can be used to get stock quotes in interesting ways, and in bulk, e.g. from Google Finance.
Note - currently broken, I'll fix and update.
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A github package for displaying markdown from within code cells in IPython output, while ignoring it when run in normal Python (like usual comments).
Code and Installation, notebook and .py examples -
Save image from clipboard to a jpg file without needing an image processing program.
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print_metrics Print color-coded TensorFlow metrics in IPython for Training, Validation and Test data or datasets in tabular format, comparing those with the another metrics (such as previous history), if any. -
It's a combination of file explorer and file-search utility.
For integration into Windows context menu, run it first time as administrator.
It has some cool features still absent from Windows explorer search, such as multi-directory search, using max depth and file size, hot-search, showing tree-view in the results, exporting to Excel or html etc.
Blender, manim, desmos, dot and Voicemeeter are the tools I've primarily used for animation.
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Create private equivalent of a fork of a public repo on github.
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Code, develop, build, test and showcase like a Pro.
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A truly portable and secure solution for managing your 2FA accounts!
No need to worry about losing your laptop, phone or changing from Android to iOS or vice versa. -
Setup audio both with and without an external dock.
Force detection of audio devices including speakers and microphones.
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While in Monty Hall Paradox Test Tool I focused on explaining the working of this paradox through its results, here I have derived the equations for the generalized case (for any number of total, opened and winning doors).
Nice to see how the results match the equations. -
Multiple chapters.
Trying to explain how machine learning is a natural progression of conventional regression using the rapidly advancing processing power, and some of the neat small mathematical tricks we had or discovered in the last century.
This, while also shaping up the constructing ideas behind it from the ground-up - for the semi-initiated. -
If you work in, or have interest in Information Technology or Mathematics, then Information Theory is something you must be acquainted with.
If you haven't heard of it, check the section Information Theory - Quick Introduction on the linked page.
What I have provided on this page is an intuitive build-up which is generally not present in most explanations of the Information Theory - including the one that came from its founder. -
If enlightenment came in pieces, I bet the realization which I had and which I've provided on this page, would be one of those!
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On a cursory look, MAD seems to be perfect – we want to know – on an average – how far each of the numbers in a set of observations is from their mean (M), and MAD tells us exactly that.
Then what is the problem? Why don’t we use MAD everywhere instead of σ? -
A little visualization for understanding this basic stuff. You can also look at this animation for a quick overview.
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With a Thrust-to-Weight Ratio of Less Than 1!
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Multiple Explanations
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If the idea of tensors ever intrigued you and you wanted to get to the real physical understanding of them, this article might help.
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A humble attempt at explaining the relativity of physics and the physics of relativity, with special treatment to vector analysis.
Some knowledge of calculus is required.
- Visarga ( ः), ह and ह् are indeed disparate in Sanskrit, and so are anusvara ( ं - ṃ), न, न्, म and म्.
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See if you can make any sense of it. I couldn't since the time I wrote it.
It did make some sense before I conceived it though! -
A few short motivational stories, retold.