BC GENERICLEARNINGS
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๐Ÿงต๐Ÿงต๐Ÿงต๐Ÿงต ๐Ÿ‘‡

Artificial Inteligence = ๐Ÿ–ฅ๏ธ + ๐Ÿง 

When can you expect your fridge & mobile phone to revolt against you? (Spoiler Alert: Not in the foreseeable future).

Please Do 're-tweet' the Thread & help me spread the knowledge to more people.

 1. The Language
In order to understand Artificial intelligence, machine learning, deep learning, we need to start at the very basics.
Humans speak and understand a wide variety of 'natural' languages (this is what computer science community calls them): like English, Hindi, Marathi, Bengali, Tamil etc.
But have you seen a peculiarity of human languages? They do not have a precise meaning associated with a given sentence. On an average, every English word has 2 meanings. This makes human conversations confusing, with the need for multiple turns & clarifications. Example ๐Ÿ‘‡:
Person 1 conversing with Person 2 face to face:

Person 1: Lets go watch The Dark Knight. Its our marriage anniversary!
Person 2: I dont like going out at night. Its too scary & depressing.
Person 1: Oh, what i meant was watching the movie "The dark Knight".
Person 2: Yeah yeah, sure. Let's all get together and watch ""the dark knight"". Because that is what everyone loves watching on their anniversary.
Person 1: Thanks honey, you're the best :D.
Person 2: Are you serious right now?
Person 1: As serious as Harvey Dent. As serious as Gautam Gambhir's surname.
Person 2: Okay, I dont know who they are, and obviously I was being sarcastic.
I hope the reader can appreciate that misunderstandings & miscommunications are a part & parcel of human language. We certainly did not & really more importantly did not know how to build our computers in a way that they understand all this ambiguity.
So we gave them the most basic language possible. Binary.

But its more than that. This language, in which computers talk, every string has a unique precise way to understand the 'sentence' (also known as a computer program). No ambiguity. No Ambiguity.
print("Hello World!")

This is the stereotypical 1st program which any person new to Computer Science writes in one of the computer programming languages: Python.
Summary: Humans have an ambiguous uncertain way to communicate. Computers communicate in a language where each 'sentence' or 'conversation' (also known as a computer program) has a unique meaning.
2. The two schools.
As old as our tryst with computers is our yearning to see them do 'intelligent' things. This was the 1st time that humans have turned God. And we wanted to build our prodigy in our own image. Give it the gift of intelligence.
This gave rise to the field of Artificial intelligence (AI).

Artificial Intelligence: Any style of programming which enables computers to mimic humans.
The academic circles were somewhat divided in opinion in how they would go from computers with simple programs to computers which could demonstrate 'human like' behavior.
As an aside, one of the accepted tests of human like behavior (an Agni pariksha if you will) is what is known as a 'Turing test' named after the Father of computer science Sir Alan Turing.
In Short, a turing test is successful if a computer can convince a human evaluator that it is indistinguishable from a human. They communicate through a medium like web chatting.
Back to the division. There were two schools of thoughts on how AI would be created.
1. The Symbolism school of thought: Cutting out all the jargon, they thought that AI would be created by programmers by codifying explicitly in a program exactly how it should respond to each situation it is presented with.
This is not as bad as it sounds. Computer programs compose, just as math functions.
so, through a limited set of atomic or basic programs we can represent an exponential set of outcomes.
2. The connectionist school of thought: This school of thought argued we already have a working example of intelligence: The human brain. They wanted to mimic the human brain & anatomy into the computer systems. Make the prodigal child in the parent's image.
If we look at the human brain, it seems to be a network of atomic units called Neurons. What is a network? An web of nodes interconnected by edges. Neuron cell bodies are the nodes, the dendrites are the edges.
The connectionists had a Eureka moment. 'Why don't we try to mimic/simulate this in computers'! One of the very first advances here was 'Perceptron' by Frank Rosenblatt.
As an aside, the one of the pioneers of the field, Marvin Minsky who also started the AI lab at MIT wrote a book on perceptrons.
This book is the center of a long-standing controversy in the study of AI. It is claimed that pessimistic predictions made by Marvin were responsible for a change in the direction of research in AI & contributed to the AI winter of the 1980s, when AI's promise was not realized
Perceptron is literally a mathematical model of the human neuron. Of course, the human neuron is much more complex than a perceptron (How much more? Read on to find out).
A perceptron literally just sums its inputs after weighing them, applies a function (typically a non-linear function in the modern era) on top of it, and voila, you have a simulated, or artificial neuron.
What you see in front of you is a mathematical model. How do you go from here to a working code? You need something called an algorithm. Which can help us decide the 'weights' of the perceptron. This is the perceptron learning algorithm.
Good resource: https://t.co/Yn7pV45yip
3. From one to many
Now that we can simulate a neuron, what is the next step? Of course, to simulate an interconnected web of neurons! These are what are known as artificial neural networks.
These early multi-layer artificial neural networks are also known as feedforward neural networks because they, 'feed forward'. if you know some math, you might see vectors and matrices inherent in these networks.
These networks needed a new training algo, because they are a different, more powerful mathematical model than a perceptron due to the composition. How much more powerful? Glad you asked. :)
Enter, the universal approximation theorem.

It turns out, single hidden layer feed forward neural networks can approximate ANY* function to arbitrary degree of precision.

*terms & conditions applied.
https://t.co/WFRooX53Sx
This is as powerful as it gets. A single hidden layer FF neural network is all you need. You're done. Not so soon :)
While the theorem proves that such networks exist for each function and degree of precision, it says NOTHING about how to find them.
And that, remains the hard part. The learning algorithms for these things is essentially something a 11th class math student would understand. Its the good old differential calculus. This has a fancy name called back-propagation.
4. The backprop Algo.

Let me describe this algorithm in few easy steps. I will try to keep it as non-mathematical as possible since many readers might have a non-mathematical background. Some math is unavoidable thought.
1. First, you need to define a problem to apply FF neural networks on. The canonical example here is the MNIST example.https://t.co/jKn5cs6t0d
MNIST consists of a dataset of hand written digits. Model needs to identify which digit this is. You can imagine that optical character recognition programs are built on top of models like these.
2. With the problem defined, you need to define an encoding for the output. In this case, we represent the output as a 1-hot vector. If the digit was 3, the vector is: [0,0,0,1,0,0,0,0,0,0]
https://t.co/AfxlveCec6
3. Now you can imagine, that the model outputs a probability distribution over the 10 possible outputs. Eg: Model might say: with 90% probability it is the digit 1. With 10% probability is is 7. This
4. Now what we need is a way to judge whether model output was correct or not. We have 2 probability distributions in front of us: First, the model output. Second, the 'ground truth' distribution: the one-hot vector.
Enter the 'loss function'. This is also called the objective function. Because this function defines how the model behaves. Since we have 2 probability distribitions, maths has many functions to measure the 'distance' between these distributions.
Pick any one such function, then you have your loss function. The 'loss' is the distance between true probability distribution & model predicted one. Objective is to minimize it.
5. As long as all our functions were differentiable, differential calculus can tell us how to compute the derivative of any 'weight' in the model with respect to the final loss function.
This is the back-prop step. Because you converted a final loss function into derivatives of the weights.
5. Then. we make a small step in the direction of the derivative, adjusting the weight of the network ever so slightly in the direction of the derivative. What this does is 'tunes' the weight and model output moves towards true distribution ever so slightly.
This is known as a training step in ML parlance. Do this enough number of times, & voila! You have a trained neural network which recognizes digits.
There are readymade programs available on Github which do this. If you have a laptop, you can tinker with these & play with them:
https://t.co/yTZ9W55ncy
5. From ANN to modern Deep learning.

We have known about ANNs for many decades. Why then did we have to wait until 2010s before seeing wide ranging applications?
Well, it turns out that for these systems to be useful, 3 ingredients were needed:
1. Lot of data. If we're learning from data (this is the core of machine learning), we need lot of data! To capture all the variations in data. https://t.co/9x4qnTAWB4
2. Lot of compute. Training a neural network is so intensive (matrix multiplications are heavy duty lifting after all) that companies how build custom computer hardware just to train neural networks. https://t.co/GzAR6udF3P
3. We needed the undying human spirit which refuses to give up even in the face of near certain doom. The pioneers of deep learning spent all their lives being ridiculed for focussing on something which never really seemed to work. They did not give up.
https://t.co/NZDi4fYpU6
The first 'aha' moment of modern Deep learning happened in 2010 in the ImageNet competition. A competition which was held every few years to advance the state of image recognition.
https://t.co/tudLnUAhHh
THis moment was not a triumph for science alone. It was a triumph for engineering. The science was known since 1960s perfected over 50 years. But it took a giant engineering effort to make it work in reality.
Science dreams how our world should be. Engineers work hard to realize that dream. Society has a need for both kinds of contributions.
Near 2010 they reduced the State of the Art (SOTA) error rates from 25% to 15% which was one small step for the authors, but one GIANT leap for humankind & deep learning.
6. Wait but what about ML?
I'm been untruthful when i talked about 2 schools of thought in AI. There are several. What predates modern ML and developed alongside the other schools are traditional ML methods. Everybody loves talking about the winners.
That is why this thread is about connectionists. This is what really really works very well. Traditional ML techniques work, but DL trumps them in almost all ways. Still for someone curious enough, here is the bible of ML:
https://t.co/ylraMZQips

We did parts of this in college.
7. What has Deep learning/Machine learning done for me?

It wouldn't be an overstatement to say, everything.
Google, Facebook, Instagram, YouTube, Google Photos, Twitter everything is powered by Deep Learning. Deep Learning is eating software, quite literally.
1. Recently, OpenAI launched a thing Called Codex.
https://t.co/SXBoEXQuma

You can provide this program english description of what a program should do, and it codes that program. That day might not be far when standard programming is 'deprecated' by Deep Learning models.
2. One of the biggest problems in Computational biology is to figure out the structure of proteins. This is called the Protein folding problem. This will enable great advances in the field of drug discovery.
https://t.co/7yz7ydUL0a
3. Another space Deep learning has really excelled is Games. Chess, go. These are really complex games. Deep learning techniques are now so advanced, nothing even comes a close second. Not traditional programming methods, not humans, by a mile.
https://t.co/AprUFaYKJE
4. Tesla Driving AI. If you havent seen Tesla Full Self Driving beta demos, you HAVE to watch them. Self driving cars are far far nearer than anyone imagines. Definitely in the west. I wouldnt be surprised if that is the case in India as well.
https://t.co/SQOQAPUvBL
8. Where can I learn ML/DL?
So many great places. But in my opinion, best one is courses. There are many of them, all of them great. My Masters guide Ravindran sir has free NPTEL courses.
https://t.co/aIjSK13v0F
There are also live versions of the source where one can do assignments, get graded, learn more closely with other people. Learning has come to you, knocking your doors. Now its up to you whether you get it come inside or not :D
There is of course the Stanford coursera ML course by Andrew Ng :
https://t.co/LwE94cwzoe

for those that prefer the gentler, simpler, easier version.
For those that want to listen to more philosophical discussions & history of AI, I cannot recommend @lexfridman podcast enough. It has technical details too, but is more advanced.
9. Can AI revolt against us?

This is something which has worried many people. The short answer is, not in the foreseeable future. Computers are still quite dumb. They do what they're told. Remember objective functions? That is how you tell a DL model what to do.
The concept of revolting against humans does not make any sense unless someone has explicitly programmed them for that. In which case, it isnt a revolt :). We discovered nuclear power & for a while tried to blow ourselves up.
AI needs clear government regulations which prevent that. At the same time, the potential of AI is literally unlimited. It is going to help us discover new materials, new drugs, new ways of tackling climate change, new microorganisms which eat plastic.
Software is eating the world. And Deep learning is eating software.
But thanks to awesome tools and platforms, everyone has the ability to make every day, an AI day. :)

If you like the thread, please retweet the very 1st tweet so that this interesting information reaches as many people as possible.

More from Sahil Sharma

Sahil Sharma
Sahil Sharma
@sahil_vi
#neulandlab ๐ŸŒ‹๐ŸŒ‹ read on to understand this company and its story of transformation. On twitter, ๐ŸŒ‹ has become synonymous with neuland, and this is how i will use it throughout the thread. If you like the thread, plz retweet & help fellow investors learn


Starting with a simple primer on the Pharma industry. They make medicines for all of us. The part of the medicine which gives it its potency against the ailment it cures is called API: Active Pharmaceutical

(here is a reference few people will get, but have to make it since I love it :D):
If Guardians of the globe (https://t.co/vH2C1TDbPy) were a medicine, Omniman (https://t.co/0DGb1MAtXN) is the API.


Back to ๐ŸŒ‹. There are pharma companies which sell APIs as well as the finished medicine (FDF: Finished Dosage Formulation). Neuland are pureplay API manufacturers. This provides them some advantage since they don't compete with their clients (the FDF makers).

Medicine is diverse. There are medicine which were first discovered in 1920s (https://t.co/6DXNrhOK46), to those being created now (https://t.co/tcr7dAexmO). Naturally then, some of this involves very complex chemistry to manufacture, others are relatively easier to manufacture.
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Sahil Sharma
Sahil Sharma
@sahil_vi
Such opportunities only come once in a few years.

Step-by-step: how to use (the free) @screener_in to generate investment ideas.

Do retweet if you find it useful to benefit max investors. ๐Ÿ™๐Ÿ™

Ready or not, ๐Ÿงต๐Ÿงตโคต๏ธ


I will use the free screener version so that everyone can follow along.

Outline
1. Stepwise Guide
2. Practical Example: CoffeeCan Companies
3. Practical Example: Smallcap Consistent compounders
4. Practical Example: Smallcap turnaround
5. Key Takeaway

1. Stepwise Guide
Step1

Go to https://t.co/jtOL2Bpoys


Step2

Go to "SCREENS" tab


Step3

Go to "CREATE NEW SCREEN"

At this point you need to register. No charges. I did that with my brother's email id. This is what you see after that.
SCREENERS , SCREENER , SCRRENER TOOL
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Sahil Sharma
Sahil Sharma
@sahil_vi
Lets discuss how to invest in equity directly, the end to end picture, taking as an example my largest holding: RACL โš™๏ธ ๐Ÿ–ฅ๏ธ

๐Ÿงต๐Ÿงต๐Ÿ‘‡๐Ÿ‘‡


I am not a financial advisor & please donโ€™t treat any of this as financial advice. Also, please dont think of whatever i say here as gospel or the absolute truth. This is only my perception of the truth.

I am not your Guru.


A little bit of framework/paradigm building up-front, please bear with me.

Here is the 1 tweet summary. 1000s of hours of reading, writing & contemplation.
Then, decades of patience. If we get both the steps roughly right, then some ๐Ÿ’ฐ๐Ÿ’ฐ.


The first step a Direct Equity investor with little knowledge has to do is to perceive their world accurately.

We do this by seeing interviews by experienced investors, books they're writing & reading company communications.


When we perceive our world through reading & listening, we go from being an investor with little knowledge to an investor with some knowledge. We are building mental models by learning from experienced investors

See how many our investor became :D
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Sahil Sharma
Sahil Sharma
@sahil_vi
This thread is to create awareness on how to use
https://t.co/3jeqlXO0QH

Please note that i am not officially associated with website. Only an active contributor & hope to benefit from network effects of interested investors actively contributing on platform.

https://t.co/64yVScplqi is above all else a community of like minded investors who wants to actively engage in understandin at a fundamental level & separate wheat from the chaff.

Develop an understanding of the biz, industry, competitive intensity, management, valuation

Website was created > 10 years ago. Early users are seasoned investors & i personally look to learn greatly by following their footprints across the website.

There are broadly speaking 4-5 types of "threads" or discussion places across the website.

1. Featured discussions (hall of fame, showcase discussions, learning).

These enable investor to level up the most. These discussions are a showcase for the website itself.
These are also a great starting point. Since VP is not a course, feel free to traverse them in any order
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Sahil Sharma
Sahil Sharma
@sahil_vi
Given the immense interest in this #investment #process thread about how to get investment ideas, thought of creating a logical follow-up ๐Ÿงต

Given that I have an idea & some sense for valuation & growth, how do i construct a portfolio?

If you like the thread, plz RT.


First thing worth mentioning is that I am only describing my own process. Others can follow a different process which could work for them.

Opposite of a good idea can also be a good idea

Before understanding the process itself, it is important to understand the problems the process is being designed to solve for. What are the key hurdles or troubles I have faced in designing my PF.

Hurdles
1. At any given point in time (in the past) i have only known deeply about 5% of the listed universe in India, or lower. When I say know deeply I mean read an annual report, an investor presentation, a concall, understood why the business is how it is.

2. I only started my corporate life 4 years ago, so my pf size is small compared to yearly savings. This means that I need to deploy large incremental capital every year at least for next few years. This cant be a buy & track pf, this is a keep buying & tracking for a few yrs pf
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Aditya Kondawar
Aditya Kondawar
@aditya_kondawar
A list of all the articles that we have created - From Banking to Fiber!

The content in the articles has been written in a very simple language which will help you learn everything about the sector and/or the company!

Do Retweet and help your fellow tweeples learn!

Let's go ๐Ÿ‘‡

Starting with Financials -

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https://t.co/tMfB73CHYs

Top 5 Banks and their strategies -

https://t.co/aivfUtuw9g

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How did they avoid all NPAs from 1999? What makes Uday Kotak's Concalls a goldmine of information on the Banking sector? Everything explained!

We have given details from 1999! The most comprehensive article ever!

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Bull Markets
Bull Markets
@BullMarkets1
Some useful websites for investing-

Step up SIP Calculator-
https://t.co/Wa6oaBgHac

https://t.co/Nt5e8l3k3M

CAGR Calculator-
https://t.co/NNfy0x33eS

Stock Research sites-
https://t.co/wG26d3OMY8

https://t.co/kJb8xZI3NF

https://t.co/khNeWuZgtJ

๐Ÿ˜Ž๐Ÿ˜ŽHappy Investing ๐Ÿ˜Ž๐Ÿ˜Ž
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Abhishek Kar
Abhishek Kar
@Abhishekkar_
Fundamental Analysis Checklist
#RETWEET for wider reach
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Tar \u26a1
Tar โšก
@itsTarH
It's the weekend!

Grab a cup of coffee, in this thread I will explain

1. What is a Yield Curve?
2. Why is it an important indicator of Recession?
3. Is an Economic Recession around the corner?

Lets dive right in!


Before we understand a Yield Curve, lets get some of the basic terminology out of the way

1. What is a Bond?

A bond is a fixed income financial instrument which represents a loan made by an investor to a borrower

The investor typically receives payment in the form of interest and a lumpsum (principal) on maturity

The interest rate on a bond is known as coupon rate.


There are many types of bonds like

1. Zero Coupon : Pays both interest and principal at maturity
2. Sovereign Bonds : Issued by a Government or a Quasi Government Institution
3. Corporate Bonds : Issued by corporate like banks, established companies etc.


Coupon (Interest Rates) on a bond are decided based on the issuer's credit rating

The higher the credit worthiness of the issuer, the lower the coupon rate

Credit Rating on a Bond is usually assigned by credit rating agencies like Moody's or Standard & Poor's (S&P)
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Sahil Kapoor
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@SahilKapoor
Why & how does rising Crude Oil hurt India?

A short thread ๐Ÿงต

India is the 3rd largest consumer of Oil at 5.35 million barrels per day (MBPD) behind US (21.2mbpd) and China (15.1mbpd)

India imports nearly 85% of its total Crude oil consumption every year.

India's own production has been below 700,000 barrels per day for a long time.

India imports oil in the following share as per FY 21 numbers:

Iran 17%,
Saudi Arabia 16%,
UAE 14%,
USA 9%,
Qatar 8%,
Others 38%

India import share for Oil from Russia is under 2%, an insignificant number.

India imports about 290 Million tones of Oil in a year.

Assuming that Oil prices are at $75 (last year),
India's oil import bill is ~ $165bn

India exports refined petroleum products as well. At $75 oil price those exports would amount to $58bn

=Oil trade deficit of $105bn

Oil at:
$100, India's oil trade deficit (imports - exports) would be ~ $140bn

For every $10 rise in Crude Oil from say $100, India's oil trade deficit will rise by ~$15bn

For instance at $120 Oil price it would be ~ $170bn
GENERICLEARNINGS

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KrishnaLeela
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@Talesofkrishna
๐ŸŒฟ๐‘ป๐’‰๐’† ๐’”๐’•๐’๐’“๐’š ๐’๐’‡ ๐’‚ ๐‘บ๐’•๐’‚๐’“ : ๐‘ซ๐’‰๐’“๐’–๐’—๐’‚ & ๐‘ฝ๐’Š๐’”๐’‰๐’๐’–

Once upon a time there was a Raja named Uttฤnapฤda born of Svayambhuva Manu,1st man on earth.He had 2 beautiful wives - Suniti & Suruchi & two sons were born of them Dhruva & Uttama respectively.
#talesofkrishna https://t.co/E85MTPkF9W


Now Suniti was the daughter of a tribal chief while Suruchi was the daughter of a rich king. Hence Suruchi was always favored the most by Raja while Suniti was ignored. But while Suniti was gentle & kind hearted by nature Suruchi was venomous inside.
#KrishnaLeela


The story is of a time when ideally the eldest son of the king becomes the heir to the throne. Hence the sinhasan of the Raja belonged to Dhruva.This is why Suruchi who was the 2nd wife nourished poison in her heart for Dhruva as she knew her son will never get the throne.


One day when Dhruva was just 5 years old he went on to sit on his father's lap. Suruchi, the jealous queen, got enraged and shoved him away from Raja as she never wanted Raja to shower Dhruva with his fatherly affection.


Dhruva protested questioning his step mother "why can't i sit on my own father's lap?" A furious Suruchi berated him saying "only God can allow him that privilege. Go ask him"
RELIGION
Vibhu Vashisth
Vibhu Vashisth
@VIBHU_Tweet
RISHI PINGALA:THE ANCIENT INDIAN SAGE DISCREDITED FOR HIS MAJOR CONTRIBUTIONS TO MATHEMATICS & PHI

Have you ever heard about,"The Golden Ratio"?
The Golden Ratio or Phi(Greek) is a mathematical ratio of nature. Mother Nature is the most exquisite artist in herself.


&Its this Phi she uses to create all the beauty in World.
If anyone uses this ratio correctly in their art,it can turn into a spell-binding piece of work.Such is the significance of'Phi'.But my thread isn't about it.Its about somethin called'Fibonacci Series'&who actually made it


Phi is related to Fibonacci series(0,1,1,2,3,5,8,13,21,34...)
&this series is responsible for everything dat is beautiful on this planet. Just try to calculate the ratio of any 2 successive numbers รท each of them by the number before it in above infinite series.

E.g. 3/2=1.5, 5/3=1.666, 8/5=1.6, 34/21=1.61904 and so on. Did you notice something? The ratio is coming almost same every time. This ratio is called 'Phi 'which is numerically represented as 1:1.618. This Golden Spiral is made in exact proportion of 1:1.618.


The thing I want to emphasize here is that we always find only those things appealing and attractive that have Phi. It's in our subconscious. Isn't it some revelation? We are the part of this Divine proportion of the God or the Mother Nature. Check these:
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Aditya Todmal
Aditya Todmal
@AdityaTodmal
The absolute best 15 scanners which experts are using.

Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX

Share for the benefit of everyone.

Listing all scanners from @Pathik_Trader Sir first.

1. Open

2. 80-20 Reversal Setup


3. Fake

4. Trapping Inside Bar (Positional Setup)
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Anu Satheesh \U0001f1ee\U0001f1f3
Anu Satheesh ๐Ÿ‡ฎ๐Ÿ‡ณ...
@AnuSatheesh5
Yoga Ramar Temple
#Nedungunam
#TamilNaduTemples ๐Ÿšฉ

Yoga Ramar Temple is located near Thiruvannamalai. Shri Rama is seated with eyes closed, without any weapons. Mata Sitaย  pointing to the feet of Rama telling thatย  everyone is safe at his feet. Lakshmana standing near Shri Rama


Hanuman is seen chanting Vedas obediently sitting before Shri Rama.

Sthala Puranam says that, Sukabrahma Maharishi had been doing Tapasya here, for the darshan of Sri Rama. Prabhu Shri Ram, pleased with his tapasya, came here after Rama Ravana yudham. Since Rama was returning


after the war, he did not have any weapons with him.

Another gopuram called Kili Gopuram is there for Sukabrahma Maharishi. There is a hill nearby called Dheergachala. According to Sthala puranam, Sri Padham of Prabhu Rama are found on the top of the hill


It is said that this is the place where Shri Rama gave darshan to Suka Maharishi. Nedumgunam means which shows all qualities of Shri Rama who is Maryadha purushottam. Shri Ramanavami is very famous here.

Jai Sri Ram
๐Ÿ™๐Ÿ•‰๐Ÿ™
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Vibhu Vashisth
Vibhu Vashisth
@VIBHU_Tweet
Did you know?
Ahilyabai Holkar was one of India's
greatest women rulers of all time.

Ahilyabai was Born on May 31,1725 in the village of Chondi,Jamkhed distt,Maharashtra to a man named Mankoji Shinde,who was the Patil(Chief) of a village and a member of proud Dhangar Community.


Despite the fact that girls' education was a farther dream in those days, Ahilyabai was homeschooled by her father.
Ahilya didn't belong to any royal family but in a twist of fate she is still remembered as one of the most revered Queen in the history.


While they were living their humdrum lives, one day Malhar Rao Holkar, commander of the Peshwa Bajirao & Lord of the Malwa stopped in Chondi on his way to Pune, where he couldn't help but notice a young 8yrs old Ahilyabai in the temple service feeding the poor & hungry.

He was so impressed by the sheer innocence&beauty with simplicity character of Ahilyabai dat he asked her hand in marriage for his son Khanderao Holkar.Just like that she got married to Khanderao in 1733 at a tender age of 8&became a bride of esteemed Maratha community of Holkars

But destiny had something else in store for her as her husband died in the Battle of Kumbher in 1754 leaving her widow at 29.Distressed Ahilyabai was to commit Sati when her father-in-law Malhar Rao refused to let it happen.
ALL , AHILYABAIITIHAS