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 : Dear Zindagi
Title : Dear Zindagi Language : Hindi Year : 2016 Director : Gauri Shinde Genre : Drama, Romance IMDB Link Watch trailer on Youtube Lead Role : Alia Bhatt, Shah Rukh Khan, Kunal Kapoor, Ali Zafar, Yashaswini Dayama, Ira Dubey
It is all about loving one's life. It is about two roads diverging in a yellow wood and taking the one commonly traveled by, because it is probably the more easier path. Kaira is the soul of the movie. She is young. She is independent. She is a talented cinematographer. And she is hot. We assume her life is picture perfect but obviously it is not. She is going through depression. She doesn't seem to know how to handle her love life. We see the reasons as the plot progresses through the sessions between Kaira and her "dimag ka doctor" Jahangir Khan. These sessions take the story forward.
The entire premise is simple, not trying to be preachy or bigly philosophical. Sometimes it shifts into a rom-com with no melodrama. Overall, the director of English Vinglish is back with another happy watch. Alia owns the role. The young actor is impeccable in character roles. Anyone who has watched Highway or Udta Punjab can vouch for that fact. The role of the mentor was a piece of cake for SRK. Music by Amit Trivedi is pleasurable, so in the visuals of the picturesque Goa.
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