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.
The year 2013. “World’s biggest movie star” Shahrukh Khan joined hands with hit-maker director Rohit Shetty and lady superstar DeepikaPadukone to bring on screen a film by the name of Chennai Express. As the name suggests, the major portion of the story was set in the south, especially in Tamil Nadu. Mindless humor, illogical action and horrendous typecasting were generously thrown in. The critics slammed the movie and its makers. Yet, the aam-aadmi lapped it up, and Chennai Express turned out to be the biggest grossing film of the year. The pattern continued in 2014 with forgettable Happy New Year and in 2015 with the disastrous Dilwale. King Khan scooped so low that he won the infamous Kela Awards, something presented to the worst performer of the year. People started to write him off as a serious actor.
Fast forward to April 15, 2016. Like the tagline of Fan says, it all began with a connection. Shahrukh’s transformation from an ambitious Delhi youth to being the Baadshah of Bollywood was through intense portrayal of varying emotions in movies like Darr, Baazigar, Devdas, Kuch KuchHota Hein, Swadesh, Veer Zara, Chak De, Don and My Name is Khan to name a few. That connection between the actor and scripts with a soul was never lost. That could be the reason why the actor decided to stretch himself for the dual and contrasting roles of Aryan Khanna and Gaurav Chanda when Maneesh Sharma offered Fan to him.
SRK deserves praise for doing Fan for multiple reasons. For starters, you would not see any of his usual mannerisms or style in the role of the obsessive and ardent fan, Gaurav. Khan has pushed himself very well and has wowed the spectators with an impressive performance. Secondly, it is not a cakewalk for a fifty year old to essay the role of a teenager in a convincing manner. Prosthetic makeup, and ample use of technology including motion capture have been efficiently put into use. Then the point of consideration is that unlike any of his recent movies, Fan is not a purely commercial venture, with unnecessary masala not added to spice up things. People say an elegant heroine and romantic songs are the USP of Khan’s films. You would see neither in Fan.
So, is Fan a perfect product? A classic? Unfortunately, not so. The storyline is loosely what comes to a person’s mind as they watch the trailer of the movie. An obsessive fan of a reigning superstar and how certain circumstances, places them at loggerheads. While the pre interval portion of the movie is engaging and has depth, the storyline reduces to that of an ordinary thriller in the second half. That does not make Fan a bad movie, never. It is just that the plot could have been better executed.
The sad fact is that while a Chennai Express sets the box office ringing, an experimental attempt like Fan does not find much takers. This is not a very positive sign for the movie industry, as quality of the content would decline if money flows only in the masala path. We need to stop for a bit and analyze who is at fault – is it the audience who are ready to spend bucks for colorful entertainers but are hesitant in the case of realistic movies, or does the fault lies with the filmmakers themselves, as it were they who spoon-fed the idea that exaggerated, happy ending movies are entertainment, and realistic movies are for intellectuals? Think, and more importantly, act.
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