can you vish?

 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 ...

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #2 (Regression) Machine Unlearning #3 (Clustering) Machine Unlearning #4 (Outlier Analysis) Reinforcement Learning is a slightly different learning model than the other techniques that we have discussed previously. Therefore, I wouldn’t be able to explain the same using the fruit basket example that we have been using all this while. Let’s replace apples and oranges with self-driving cars! Suppose that you are at Google or Tesla and are trying to train a car to drive by itself! How would you go about that? Driving requires the knowledge of much more than turning the ignition on and steering the wheel. You should know to keep the side of the road, to stop at the red signal, or to keep off the footpath, for instance. You decide...

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #2 (Regression) Machine Unlearning #3 (Clustering) Machine Unlearning #5 (Reinforcement Learning) Let’s start with the example. We have the basket full of different fruits and we are sorting the fruits based on their distinctive properties, or features. What would happen if a coconut got mixed in the fruit basket that contained oranges, apples, bananas, cherries, and mangoes? The coconut does not share any feature (color, shape, or size) with any of those fruits in the basket. Since there is only in coconut in the basket, it is not possible to create a separate group of coconuts. Such items in the group, which stand out or are distinctive than the others are generally termed outliers. Outliers are sometimes discarded as noise...

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #2 (Regression) Machine Unlearning #4 (Outlier Analysis) Machine Unlearning #5 (Reinforcement Learning) We have already gone through classification and prediction. Now let us see what clustering is. Another popular learning technique, clustering is different from the other two since it is an unsupervised learning technique. What does that mean? Let us revisit the classification technique. We show the machine an Orange and explains the features of the Orange to it. Similarly, each different fruit and its features are shown to the machine during the training phase. Once it has learned enough, we use the machine to label a randomly picked fruit. In clustering, such training does not take place. We present the system with a baske...

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #3 (Clustering) Machine Unlearning #4 (Outlier Analysis) Machine Unlearning #5 (Reinforcement Learning) Regression is a popular machine learning technique used to predict target data based on a set of features. In classification, we train the system to assign distinctive labels to the object (Orange or Bananas in our previous example). Regression differs in the sense that here we are dealing with continuous variables. Suppose that you have twenty thousand rupees with you and are planning to buy a new phone. You open up an e-commerce site and search for phones. A hundred results appear. You see names like Redmi, Oppo, Vivo, Realme, Asus, Nokia, and so on, all in your target range of ten thousand to twenty thousand. You are cle...

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...

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. W...

Recently, there was some heated discourse in the social media after trolls appeared abusing a firm for translating subtitles of popular TV shows and revered movies in Malayalam. The now infamous term ‘colony’ was tossed around to bring about a class structure in entertainment. Apparently, the shows like Breaking Bad or Money Heist or movies like Inception were to be appreciated only by those who knew English. Reminded me of the racist Americans who go around saying “if you don’t speak English, go back to your country”. While, ideally it should have been “if you speak English, go back to England”.  If they are purists and have a problem with subtitles in Malayalam, then they should have installed Dup and started learning Italian before humming “Bella Ciao”. Anyway, I do have some history with subtitles. Nothing much, except that I have been asked to do the subs for some of the short films my friends have made.  I do not know how that offer came about, cause I have nev...

I remember me who would not touch a black tea. This was back when I was a dumb kid (Well I am not a kid anymore am I!). For me, tea meant only the one with milk, water, sugar, and tea leaves of course. And I needed a glassful of it at least twice a day. If for some reason, we had run out of milk, I would simply skip having tea that day. Whenever my parents offered me black tea, I would staunchly refuse. Years passed, and I was duly introduced to the intoxication of the black tea. I had just joined TKMCE, and Parvathi dragged me to this lovely chechi used to sell tea and snacks. I remember hesitatingly taking a sip of lime tea, and falling in love with it almost instantly. More days, more tea. A couple of years passed, and I found myself at Trivandrum with work commitments. Lured by the promise of delicious mutton dishes, I made a solo visit to Rajila Hotel. With the finger-licking good offerings, they only had their trademark mint-lime sulaimani to wash it all down. It was the f...

വെൽക്കം റ്റു കലിയുഗം  എല്ലാര്ക്കും സുഖം  എന്ന് കരുതുന്നു  മിണ്ടാനൊന്നും നേരമില്ല  വീട്ടിൽ പോയി  ചിൽ ചെയ്യാൻ ഉള്ളതാ  ഞങ്ങൾ ഫെയിസ്‍ബുക്കിൽ പോരാളികൾ  കമന്റ് ബോക്സിൽ നിറയെ പോർവിളികൾ  പട്ടം ചാർത്തി കൊടുക്കും  പൊട്ടൻ എന്ന് വിളിക്കും  നൂറ് "ഹ ഹ" കിട്ടിയാൽ  പുളകം കൊള്ളും  വെൽക്കം റ്റു കലിയുഗം  എല്ലാര്ക്കും സുഖം  എന്ന് കരുതുന്നു  മിണ്ടാനൊന്നും നേരമില്ല  ഇനിയിപ്പോ സുഖമില്ലേൽ  യോഗാ ചെയ്ത്  രോഗം മാറ്റാം  കിട്ടിയ ലൈക്ക് കൂട്ടി വെച്ച്  അഭിമാനം കൊള്ളും  വാലിഡേഷൻ പീക്കിൽ നിന്ന്  ഓർഗാസം നുകരും  ഞങ്ങൾ ഫെയിസ്‍ബുക്കിൽ പോരാളികൾ  വൈറൽ പോസ്റ്സ് ഞങ്ങടെ നീർച്ചുഴികൾ  വെൽക്കം റ്റു കലിയുഗം  എല്ലാര്ക്കും സുഖം  എന്ന് കരുതുന്നു 

ഈ ഭൂമീടെ സ്പന്ദനം കണക്കിലാ എന്ന് ചൊല്ലിക്കൊടുത്തു നാം കുരുന്നുകൾക്ക് തോളിൽ മാറാപ്പും ഏറ്റി അവർ കയറി ചെന്നു ഗണിതം പഠിക്കാൻ ഗുണനം ഹരണം പൈതഗോറസ് തിയറം ഹൈഡ്രജൻ നൈട്രജൻ ക്രോമോസോം ഈസ്ട്രജൻ എക്സ്ഉം വൈയും കണ്ടു പിടിച്ച് സമയം പോയപ്പോ നേരെ പാഞ്ഞു ട്യുഷന് ലോസ് ഓഫ് മോഷൻ പഠിക്കാൻ ഒടുവിൽ വീട്ടിൽ എത്തിയാൽ ദേ വരുന്നു ഹോംവർക്ക് ഞായറാഴ്ച്ച കിടന്നുറങ്ങാൻ പറ്റില്ല അന്നുണ്ട് ഡാൻസും പാട്ടും പെയിന്റിങ്ങും  അങ്ങനെ 5 8 ആണ്ട് പോയി മറഞ്ഞു ട്യുഷന് പകരം എൻട്രൻസ് ആയി സ്‌കൂൾ കഴിഞ്ഞു വീട്ടിൽ പറഞ്ഞു കലാകാരൻ ആകാം. നിനക്കു ജോലി വേണ്ടേ ? കഞ്ഞി കുടിക്കണ്ടേ ? കല്യാണം കഴിക്കണ്ടേ? നിനക്ക് ജീവിക്കണ്ടേ? കല കാര്യം അല്ലേൽ എന്റെ സൺ‌ഡേ കളഞ്ഞത് എന്തേ? എന്ന് ചോദിച്ചപ്പോ കലാ തിലകം ആയാ പോയിന്റ് ഉണ്ട് ഡിഗ്രീ അഡ്മിഷന് എന്ന് അങ്ങിനെ ഞാൻ എൻജിനിയർ  ആയി. എന്റെ ചങ്ക് ഡോക്ടർ  ആയി ബാക്കി ഉള്ളോൻ ബാങ്കറായി ഞങ്ങൾ എല്ലാം വൈറ്റ് കൊളറായി മാസാം മാസം പൈസ വന്നു. ദിനം ദിനം സ്‌ട്രെസും വന്നു. എന്നും നെട്ടോട്ടത്തിലായി. ഞാനും നീയും യന്ത്രങ്ങളായി ഡോക്ടർക്കും ബാങ്കർക്കും പ്രതീക്ഷ മങ്ങുമ്പോ അകെ ചുറ്റും ...

അത് എന്ത് കൊണ്ടായിരിക്കും? ആക്ട് 1 : നമുക്ക് ആ ജനൽ ഒന്ന് തുറന്ന് അപ്പുറത്തേക്ക് നോക്കാം. പാശ്ചാത്യനാട്ടിലേക്ക് നോക്കിയാൽ ദേ അവിടെ 65  കാരനായ ജെയിംസ് കാമറൂൺ എക്കാലത്തെയും ഹിറ്റ് ചിത്രമായ അവതാറിന്റെ സീക്വലുകൾ പ്ലാൻ ചെയ്യുന്നു. 73 വയസ്സുള്ള സ്റ്റീവൻ സ്പിൽബെർഗ് മികച്ച ചിത്രങ്ങളായ ബ്രിഡ്ജ് ഓഫ് സ്‌പൈസ് (2015), റെഡി പ്ലെയർ വൺ (2018) ഒക്കെ പുറത്തിറക്കിയത് ഈ ദശാബ്ദത്തിലാണ്. ഐറിഷ് മാൻ സംവിധാനം ചെയ്യുമ്പോൾ മാർട്ടിൻ സ്കോർസെസെ യുടെ പ്രായം 75  കഴിഞ്ഞിരിക്കുന്നു. ഈയിടെ ഇംഗ്ലീഷ് സംവിധായകനായ കെൻ ലോക് ഇന്റെ രണ്ടു പുതിയ ചിത്രങ്ങൾ കാണാൻ ഇടയായി. 2016 ഇൽ പുറത്തിറങ്ങിയ ഐ, ഡാനിയൽ ബ്ലേക്ക് എന്ന ചിത്രവും 2019  ഇൽ സോറി വി മിസ്സ്ഡ് യൂ എന്ന ചിത്രവും ഹൃദയസ്പർശിയായ രീതിയിൽ ആണ് ചിത്രീകരിച്ചിരിക്കുന്നത്. 70 വയസ്സുള്ള സ്പാനിഷ് സംവിധായകൻ പെഡ്രോ അൽമൊഡോവർ, 60 വയസ്സുള്ള ഇറാനിയൻ സംവിധായകൻ മാജിദ് മജീദി, 59 എത്തിയ ദക്ഷിണ കൊറിയൻ സംവിധായകൻ കിം കി ഡുക്, ഇവരൊക്കെ സാമൂഹിക പ്രസക്ത്തിയുള്ള, മേന്മയുള്ള ചിത്രങ്ങൾ ഇപ്പോഴും സംവിധാനം ചെയ്യുന്നു. ആക്ട്  2: ഇനി നമുക്ക് ജനൽ അടച്ച് അകത്തേക്ക് നോക്കാം. എവ...

Twenty-second of July ended my dance with Thiruvananthapuram, at least temporarily. How weird is it to begin writing something by describing the end!  you might ask. It gives some context, doesn't it? It tells you that something has happened. For, something has to happen before it can end. It began in 2016, my tryst with the state capital. I still remember my former self, distressed about leaving my college before the course actually ended in order to sign up for my first job. Beware!  they said. The people at Trivandrum are the worst!  they said. Communities tend to get meaner as we go down south!  they said. They didn't tell me how stupid it is to go with hearsay and shun a people just like that, even without actually interacting with them. Sigh! A wannabe city that refuses to give up on its traditional roots. If someone asks me to describe the city in one sentence, this could be how I would do it. If they ask me to describe it in a group ...

“Out beyond ideas of wrongdoing. and right-doing there is a field. I'll meet you there.” - Jalal Ad-Din Rumi To travel beyond the borders drawn out by men has always been a desire. It’s not easy. You need time. You need good company. You also need money, and not peanuts. I was at my workplace, in the interlude between the coding phases when Vignesh pinged me. ‘We are going to Thailand! Are you coming?’ Wow. For someone whose previous trips were to Goa and Gokarna, the charm that Thailand offered was too hot to decline. ‘Cool. When are we going?’ ‘The second week of April. There are public holidays coming so we could save our leaves.’ I checked the calendar. No, there was no way I could do it. For, one of the public holidays was Vishu, which is a very important family time. I could not be away from home during Vishu. ‘No. I am not coming.’ The decision was made fast. However, second thoughts lingered. I explained the situation at home...