#jubilantfoodworks @ 2443, an "abc" correction is "almost" done fm 4575

Holding 2500 & moving past 2615 would be the 1st sign of reversal attempt

Only +ve factor "Highly oversold"-meant for aggressive traders
Conservative traders wait for some "Bull candle" formation in Day t/f https://t.co/NbwmqJxo0W

One thing that goes unremarked about Wikipedia: the vast majority of the content was written over 12 years ago—which means (given turnover) that the editors dominant today were not directly involved in that creation.

Put another way, the editors who built the dominant nodes in this network...

...have little overlap with the ones who made this much more recent managerial flowchart.

Internet time runs at hundred-fold speed—the difference between the people who painted what's in the Uffizi, and the people in charge of keeping those paintings from deteriorating. Very different tasks, and (one presumes) very personalities as well. @PaulSkallas?

WHO ARE VALAKHILYAS?

Valakhilyas are 60000 Rishis who are thumb-sized. They were born to Kratu and Kriya. Rig Veda says that they sprang from the hairs of Brahma.
They move in front of Surya dev’s chariot as Sun moves daily and
shine like brilliant lights due to severe tapasya.

Tamil literature says that the main task of Valakhilyas is to prevent human beings from being scorched. They absorb the excess heat from the sun by travelling in front of him. They, in turn, get energy from Skanda and Ma Durga. The food of Valakhilyas is only wind.
@SriRamya21

Once, Rishi Kashyap performed a yagna and invited all the Devas and Rishis to help him. Indra brought a whole mountain of trees for firewood. The Valakhilyas were hardly able to lift anything but leaves. Even when they were moving leaves, they fell into rain water puddles.

Indra made fun of them. So they cursed Indra saying that his slayer would be born as the son of Kashyapa. When Indra apologized, they modified the curse that the son shall be initially an enemy of Indra, but later will become his friend.

This son born later to Kashyapa was Garuda. He flew to Indraloka to get Amrita and defeated Indra.
Later, he restored amrit vessel to Indra and was honoured as the King of birds and as the mount of Sri Vishnu.
@JumbuTweeple @artist_rama

RISHI PINGALA:THE ANCIENT INDIAN SAGE DISCREDITED FOR HIS MAJOR CONTRIBUTIONS TO MATHEMATICS & PHI

Have you ever heard about,"The Golden Ratio"?
The Golden Ratio or Phi(Greek) is a mathematical ratio of nature. Mother Nature is the most exquisite artist in herself.

&Its this Phi she uses to create all the beauty in World.
If anyone uses this ratio correctly in their art,it can turn into a spell-binding piece of work.Such is the significance of'Phi'.But my thread isn't about it.Its about somethin called'Fibonacci Series'&who actually made it

Phi is related to Fibonacci series(0,1,1,2,3,5,8,13,21,34...)
&this series is responsible for everything dat is beautiful on this planet. Just try to calculate the ratio of any 2 successive numbers ÷ each of them by the number before it in above infinite series.

E.g. 3/2=1.5, 5/3=1.666, 8/5=1.6, 34/21=1.61904 and so on. Did you notice something? The ratio is coming almost same every time. This ratio is called 'Phi 'which is numerically represented as 1:1.618. This Golden Spiral is made in exact proportion of 1:1.618.

The thing I want to emphasize here is that we always find only those things appealing and attractive that have Phi. It's in our subconscious. Isn't it some revelation? We are the part of this Divine proportion of the God or the Mother Nature. Check these:

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.

Rio de Janeiro, 1920. 🇧🇷

São Paulo, 1910. 🇧🇷

“Arpoador”, Rio de Janeiro, 1900. 🇧🇷

“Avenida Paulista”, São Paulo, 1902. 🇧🇷

Recife, 1885. 🇧🇷