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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TV Review : On Air with AIB
Title : On Air with AIB Language : Hindi/English Year : 2015 Content creators : All India Bakchod Genre : Comedy, Satire, Current Affairs, Adult Comedy IMDB Link Watch the show online on Hotspot Presented By : Rohan Joshi, Tanmay Bhat, Ashish Shakya and Gursimran Khamba
Before reviewing On Air with AIB, let us have a prologue on All India Bakchod, or AIB as they are popularly known. AIB is a group of stand up comedians who started as a YouTube channel, using humor effectively to highlight matters of significance, explaining things in very simple manner so as laymen could grasp stuff. One prominent example is when they did a video explaining the concepts of net neutrality and urged the audience to file petitions to TRAI.
Though they had a niche audience for their videos, AIB got real famous after their Roast program, which drew flak from public for its explicit content.
Coming back to On Air with AIB, this is a TV series they launched last year. Season one completed recently with ten episodes, each single episode catering to some gripping issue neglected by most of us. Each episode has different sections, titled "Can You Not", "Topical-ish" etc. English version is handled by Rohan Joshi and Ashish Shakya while Gursimran Khamba and Tanmay Bhat does the show in Hindi.
Do watch it guys. It is fun. It is informative. It is innovative. I must warn you of the liberal use of adult comedy throughout the session, but that should never be a deterrent.
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