Gold Update https://t.co/RzHJLtKMmR

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.

THE STRUCTURE, IMPORTANCE AND ADVANTAGES OF VISHNU PURAN.

Vishnu puran has twenty three thousand shlokas. It has the power to destroy all your sins. In its first part it contains the dialogue between Parashar rishi and Maitrey rishi.

The main topics discussed here are Aadikaraan sarg, origin of devtas, Samudra manthan, the vansh of Daksh, stories of Dhruv and Pruthu, Prachetas analysis, Prahlads story, the distribution of different powers to the various sections of society.

The second part consists of description of Priyavrat vansh, description of Dweeps, Varsh, Swarg, Narak, description of various planet movements, Bharat charitra, Muktimarg and dialogue between Nidhag and Ribhu.
Next is the description of Manvantars, avtaar of Vedvyas,

and ways to get freedom from Narak. Then we find a dialogue between Sagar & Aurva rishi, structure of Dharma, Shradh Kalp,Varnashram dharma, good behavior and story of Maya & Moha.

In the 4th part we find the description of Chandra vansh along with the description of its rulers.

Next are the questions regarding Krishnavtar, Gokul, the slaying of Putna, atrocities of demon Aghasur, Killing of Kansa, leela in Mathura, demons slayed by Krishna in order to ease the load of Prithvi, Krisha's marriages and sermon by Ashtavakra.

IMPORTANCE OF SATYA NARAYAN KATHA AND ITS ORIGIN.

We all have heard and read the Satyanarayan katha. But none of us dont know the original stories that Shatanand or Sadhu or Kalavati must have heard. Also, as a habit we hear or read this katha

but dont know the proper way it should be conducted.

We find its mention in Skanda puraan(Reva khand) and Bhavishya Puraan(Pratisarg parva).
Both Bhagwatam and Gita call Bhagwan Narayan as Satv or Truth. So Satyanarayan denotes this name as the one which is Truth incarnate.

The story began in Naimisharanya when Vyasji asked Sutji about a way to eliminate all the sorrows and fulfill all the desires and about who is the devta who can grant these at the same time.

So Suutji first prayed to Bhagwan Satyanarayan who is no one else but Vishnu ji.

Then he remembered all his avatars.After that he narrated the story of Narad. Once while visiting Mrityulok, Naradji was pained to see the sufferings of People around. He was deeply moved by these sufferings and wanted to know the means by which we could rid ourselves from these.

So he went to Vishnu ji and after paying his respects stood in front of him. Naradji narrated the purpose of his visit.

So Bhagwan Vishnu said to him that in Satyug and Tretayug, Bhagwan eliminates sorrow in Vishnuswarup. In Dwapar he assumes different forms to help people.

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.