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 : Departures
Title : Departures Language : Japanese Year : 2008 Director : Yôjirô Takita Genre : Drama, Music IMDB Link Watch trailer on Youtube Lead Role : Masahiro Motoki, Ryôko Hirosue, Tsutomu Yamazaki
The concepts of birth, life and death have always fascinated the imaginations of human beings. Though the scientific base to the theory is debatable, most religions around the globe preaches of an afterlife after death. Hence, when someone passes away, it is the duty of his family and friends to make sure he reaches the other world safely. In this regard, funeral ceremonies are very elaborate and pious. The astounding pyramids of Egypt bear testimony to the fact that most of us considers death as a passage rather than the end. However, the persons entrusted with the role of preparing the deceased in their journey to the next world do not often get the deserved attention in the society. 2008 Academy Award winner (for the best foreign language film) Departures tell the tales of one such man.
Daigo Kobayashi is an ordinary young man who lives a simple life with his beautiful spouse, Mika. Though he earns his livelihood playing cello at an orchestra, his career is cut short and the job of handling the dead inadvertently lands on his hands. The core of the movie is how he deals with this particular job, which is looked down by the society despite the fact everyone needs their services at some point of time. The new job does have its impact on his mental health as well as on his family life. The movie also shows the strained relationship between Daigo and his father, whose face he could not remember. Besides all these, there are very singular elements in the film that leaves space for some thinking.
The 'lady at the bathhouse', the fact that the 'living eats the dead to survive', the 'stone letter' etc are some of these. Interestingly, this movie shows the act of eating in a raw and unsavory manner.
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