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.
Lead Role : Simone Signoret, Vera Clouzot, Paul Meurisse
Just watched this two hour long movie, and I am in a dazed
state. Can’t really believe this edge of the seat thriller was visualized way
back in 1950s! For an introduction of the plot, it is about a harsh and
sadistic headmaster of a school, and how his wife and sympathetic mistress
hatch a plan together to finish him off. Well, the movie is much more than that
but I’m not going to reveal any of it and spoil the party.
The word ‘realistic’ was given a whole new dimension by the
makers. For instance, there is a particular scene that shows a couple listening
to a questionnaire program on radio. The husband tries to answer the questions,
and he looks at his wife in a proud manner whenever his answer was right. Those
who watch KBC or similar shows must have experienced similar situations. There
are no unwanted scenes, and the actors have done their job very neat. Despite
not being a horror flick, the last fifteen minutes or so would leave you with
your heart in your mouth. They have pulled off a commendable job in maintaining
the thrill up to the very last minute. Moreover, the final message to viewers
PS: Besides the suspense et al, something that left me
amused was the use of electric trains six decades back.
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