Fresh Breakout in #Reliance , RSI & MACD bullish, sustaining above today's low 2755 , the stock can rise towards 3000-3100 ahead of its earnings on 02 May

#stocks #Nifty

Not trading call, academic post

EVOLUTION OF CROWNS

The ancient Indian kings called a head crown as a Mukut and it would mostly constitute of a golden ornamental headgear like an oval cap with gems.

Sometimes it would also include a halo circle to show the divinity of the king

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As it was considered that kings were appointment by the gods themselves. Example the kings of mewar considered themselves to be the ‘Diwan of eklingji')

But in most cases very few Mukut with halo effect have been found.

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Samudragupta in the gupta era coin. You can see the helmet like headwear on him.

Till gupta era the solid golden head wear was used in most days of the monarch.

But since the onset of 8th century it reduced in usage and was now used only as a ceremonial headwear.

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Whereas earlier the Maharaja had to wear the mukut whenever his court was in session.

Still mukut was being used prominently for ceremonial activities till recent times.

Maharaj Dahir of Sindh👇

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But as times past the style of headwear also changed.

The kings mostly wore turbans ordained with pearls and gems while rarely anyone wore a true Mukut after 16th century.

Portrait of Sri Krishna Dev Raya👇

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According to @RadioZET_NEWS PKN Orlen (major Polish oil refiner and petrol retailer) is considering entering F1 as a fuel sponsor (supplier) and personal sponsor of Robert Kubica. #F1

Yesterday Kubica was at Orlen's headquarters and met with Polish Prime Minister. #F1

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 is Kuinini ‘Nini’ Manumua, the woman who was ultimately displaced by inclusion of Laurel Hubbard.

She’s 21, and it would have been her first Olympics.