As progressive as we think we have become, we’re still caught somewhere in centuries-old curse where people decide whats, whens and hows of a woman’s life. We’re told ‘how’ to behave like a girl, ‘when’ to get married, ‘what’ decisions should we take at a particular point in time and so much more.
The exact point where I am in my life is where people tell you ‘it’s time to start a family’. No matter which social-setting I am in, people always find ways to drive conversations towards this topic. Sometimes it’s actually mind boggling to figure out how a conversation that started with ‘have you heard about this new designer collection?’ ended up exact 10 mins later in ‘101 deadly consequences of not getting pregnant before you turn 30’. 🤦🏻♀️ Disappointing as it is for some, I am unapologetic for not wanting to have kids. I am unapologetic for not knowing ‘when’ will i want to have kids. I am unapologetic for keeping and following my own clock and not somebody else’s. More so, I am unapologetic for being a career-focused and rebellious woman for which there’s clearly no place in our society. The fact is, i am happy to to stand out from the crowd because being a part of the crowd is so mainstream! 💃🏻
@Regran_ed from @ab_padfoot - I screwed up. I don't know how to explain it, because it is against reason what I've done.
Let me just erase the huge mistakes I made and go back to the start of the relationship, back when everything was ok and when you still loved me, the way it was at the beginning when you were excited and wanted to know everything about me and I was your world. And I remember those times, and how it was back then... especially how you said you loved him, how you used to say it all the time. And I remember all of the magic at the beginning, how it was then, when everything seemed perfect, and I understand my mistake. How I played the part of the scientist. How I took you for granted, thinking that your love would be as reliable and logical as a machine, not even realizing that I wasn't giving you the love and understanding and emotion that you deserved. .
"Nobody said it was easy
It's such a shame for us to part
Nobody said it was easy
No one ever said it would be this hard
Oh take me back to the start"
#love#ourmoodydays#ourlives#ourlove#scientist#places#tripyoursoul#trekking#travelspired#travelgram#travel#uttarakhand_beauty#mountainlife#instabeauty#instagood#photosandpeace#your_entertainment_platform - #regrann
Science and Mountains
One of the best statistical Algorithms I'm reminded of when I'm in the mountains is the Gradient Descent Optimization Algorithm.
A person is stuck in the mountains and is trying to get down. There is heavy fog such that visibility is extremely low. Therefore, the path down the mountain is not visible, so he must use local information to find the minima. He can use the method of gradient descent, which involves looking at the steepness of the hill at his current position, then proceeding in the direction with the steepest descent (i.e. downhill). If he was trying to find the top of the mountain (i.e. the maxima), then he would proceed in the direction steepest ascent (i.e. uphill). Using this method, he would eventually find his way down the mountain. However, assume also that the steepness of the hill is not immediately obvious with simple observation, but rather it requires a sophisticated instrument to measure, which the person happens to have at the moment. It takes quite some time to measure the steepness of the hill with the instrument, thus he should minimize his use of the instrument if he wanted to get down the mountain before sunset. The difficulty then is choosing the frequency at which he should measure the steepness of the hill so not to go off track.
In this analogy, the person represents the algorithm, and the path taken down the mountain represents the sequence of parameter settings that the algorithm will explore. The steepness of the hill represents the slope of the error surface at that point. The instrument used to measure steepness is differentiation (the slope of the error surface can be calculated by taking the derivative of the squared error function at that point). The direction he chooses to travel in aligns with the gradient of the error surface at that point. The amount of time he travels before taking another measurement is the learning rate of the algorithm. .