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 : Court
Title : Court
Language : Marathi
Year : 2014
Director : Chaitanya Tamhane
Genre : Drama IMDB Link Watch trailer on YouTube
Lead Role : Usha Bane, Vivek Gomber, Pradeep Joshi
As the title seems to suggest, Court is a courtroom drama on the very outset. But it is not all about two lawyers traversing through the sections of IPC and the final verdict by the judge. Rather the movie goes on to show us glimpses of their personal lives, including their public dealings, homely chores and social circles.
A folk singer by the name Narayan Kamble is framed and brought in for a trail. He is accused of having inspired the alleged suicide of a sewage worker through one of his poems. To draw an analogy from the timeless classic 12 Angry Men, the plot focuses more on the process than on finding out if the accused is indeed guilty or not.
The debutante director has succeeded in presenting the sequences in a realistic manner. This is probably novel in Indian cinema, whose courtroom dramas are famous for punch dialogues and emotional outbursts by lawyers and others.
It is a considerable achievement for the writer-director that his first endeavor won the Best Film India Award, besides being chosen as the country's official entry for the Oscars. That said, I must say I failed in understanding why the movie was selected in the first place. As for me I did not find anything that could be described extraordinarily brilliant or out of the box. This is the second time I am being let down by a film that represents India at the Oscars. The other movie was Gujarati film The Good Road. Perhaps knowledge of the language is necessary to enjoy the finer nuances. For instance, after watching the Malayalam movie Adaminte Makan Abu, I was totally moved.
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