My personal space where I scribble whatever funny thoughts come to my mind. Actually, that is not entirely true. A lot of random thoughts enter and leave my mind all the time and the blog contains only a largely drilled down and censored subset of them.
Also, there are reviews of certain movies that have fascinated the viewer in me.
I would say the time you spent here would not be regretted.
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Movie Review : Masaan
Title : Masaan Language : Hindi Year : 2015 Director : Neeraj Ghaywan Genre : Drama, Romance IMDB Link Watch trailer on Youtube Lead Role : Richa Chadda, Vicky Kaushal, Sanjay Mishra , Shweta Tripathi
Another realistically made, thought provoking film which
keeps the viewers intrigued and in sync with the plot. Though the primary plot
is based on lost love, the movie delves into varied issues including the nation’s
debatable morality laws, social stigma, cop behavior and the like. Though the
viewers are offered a plethora of love stories by our filmmakers, seldom does
our movies paint romance in such innocent manner. The nervousness and sincerity
are beautifully conceptualized. There is a subtle jibe at the existing social
class differences too.
Richa Chadda has done a commendable job playing one of the
protagonists, Devi. Equally mentionable are Shweta Tripathi, Vicky Kaushal and
Sanjay Mishra who all lived their roles. Director Neeraj Ghaywan used to assist Anurag
Kashyap, and unsurprisingly he has chosen to follow the master in choosing rave
Personally, I have never understood why the police should
interfere with the private laws of citizens. While intercourse with mutually
consenting adults is a right, the laws related to immoral trafficking confuses
me. Incidents like the recent raids by Mumbai police at hotels to crack down on
young couples were irksome to say the least. The filmmakers have delightfully
chosen to highlight this paradox.
You might be familiar with the term Machine Learning. Worry not if you have not, cause I have tried to give a gist of the concept here. The term has been in the limelight of late and has been tossed around rather liberally to denote anything related to artificial intelligence, robotics, and data mining. Machine Learning, as the name suggests, could simply mean the field of study of enabling the “machines” (computers) to “learn” from past experiences and make informed decisions in the future. Wait a minute! Learning from past experiences is something humans do, right? Exactly! The computer folks want computers to behave more and more like us. As if there aren't enough of us already. As the machines are becoming more like us, we are becoming more like them. Introspection time! Most of us wake up every morning like clockwork! Then we rush through the morning routines - get dressed, wade through the traffic, and reach our offices or schools or wherever people expect us to be. We spe
Before The Judgement I believe I must begin by addressing the pressing question - Was planning a vacation in the midst of a pandemic a recommended move? No. Yet we went ahead with it. Here is why. We (Nithya & I) were newly married, and our vividly planned vacation at the island of Langkawi was stolen away from us by the virus. Our stay in Delhi was coming to an end due to job-related moves, and we felt it would be a waste not to utilize this opportunity in exploring at least one of the tourist hot spots easily accessible from the national capital region. Let us end this section by answering another question - Are the reasons listed above good enough to risk a vacation during a pandemic? No. We had taken a calculated risk. Arrival at Manali There are two phases to this - planning and execution. We had not started planning with Manali in mind. There were numerous choices - starting from Jaipur and Amritsar to Nainital, Shimla, and Manali. After a bit of reading and deliberations,
You can’t conclude a discussion on Machine Learning without mentioning classification. Classification is a machine learning technique where the machine is trained to predict the label of the given input data. Alright, let’s cut the jargon and get some real-world examples. Oranges and Bananas. Let’s assume that we have a box of fruits that contain some oranges and some bananas. You are asked to pick one fruit at random and tell if it is an orange or a banana. Pretty basic, right? For us, it is straightforward. We would know the answer at first sight. But, how would a computer be able to tell the difference? In classification, the machine would first be trained on some pre-labeled data. It would be shown an orange and we would tell it that the fruit is an orange. The machine would study the orange and remember its features - orange color and round shape. Then it would be shown a banana and the process is repeated. What are these features? A feature is anything that helps us uniquely labe