#TITAN 👀👀

Coming out of consolidation. Added more longs. Expecting new high.

#trading

Dhanwanthari Temple, Nelluvai
Thrissur District, Kerala.🚩🚩

ॐ नमो भगवते वासुदेवाय धन्वन्तरये अमृतकलश हस्ताय
सर्वामय विनाशनाय त्रैलोक्यनाथाय श्री महाविष्णवे नमः
#temples

Dhanwanthary Moorthy temple in Nelluvai is one among the ancient temples in Kerala.

Dhanwanthari is the Deva of Ayurveda. It is said that the murthy at the temple was installed by Ashwani Devas and it was the same idol worshipped by Sri. Vasudevar in the Dwapara Yuga. The Dhanvanthari Moorthi is facing towards the west
@SriramKannan77

Smrata-Matrarti-Nasanah', Srimad Bhagavatam states that the one who remembers the name of Dhanwantari will be cured and protected from all the diseases. Temple is known for its Oushada Prasada with Ayuvedic medicinal values, called Mukkudi.

Mukkudi Nivedyam is a very special offering to Dhanwantari. It is prepared by using dry ginger, pepper, pomegranate peel, cumin seed, turmeric, rock salt and other medicinal items. It is offered during 'Usha- Pooja', and is later on distributed to the devotees present there.

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why do I hear boss music

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

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.