Mark Hughes informuje, że Ferrari wróciło w sobotę do pakietu aerodynamicznego sprzed Singapuru. Informacje o nietrafionych poprawkach (podłoga), o których mówiłem w 101 odcinku magazynu okazały się prawdziwe. #F1pl

SRISAILAM: KASHI OF THE SOUTH (AP)
#andhratemples

The divine abode of Mallikarjuna Swamy (Jyotirlinga) and Devi Brahmaramba (Shaktipeeth).

When Ganesha was married before Kartikeya, he left in anger to Kraunch mountain. Shiva and Parvati also followed him there.

Shiva assumed the form of Jyotirlinga and resided on the mountain by the name of Mallikarjuna. Mallika means Parvati while Arjuna is another name of Shiva
It is also said that once, a princess Chandravathi, saw a cow shedding milk on a rock resembling a shivlingam.
@DubeyYash1911

She started praying to the lingam daily and used to offer jasmine (mallika pushpam) daily. Pleased Shiva appeared before her and agreed to wear jasmine garland (mallika pushpmala) on his head permanently. Since then, Shiva became known as Mallikarjun Swamy.

At this place, Shakti took the form of Bhramaram (bee) to kill Arunasur and settled as Bhramaramba Devi. The place is a Shakti Peeth as the neck portion of Devi Sati fell here.

It is said that by merely seeing the hill, one is emancipated from all his sins and worries.

हिमालय पर्वत की एक बड़ी पवित्र गुफा थी।उस गुफा के निकट ही गंगा जी बहती थी।एक बार देवर्षि नारद विचरण करते हुए वहां आ पहुंचे।वह परम पवित्र गुफा नारद जी को अत्यंत सुहावनी लगी।वहां का मनोरम प्राकृतिक दृश्य,पर्वत,नदी और वन देख उनके हृदय में श्रीहरि विष्णु की भक्ति अत्यंत बलवती हो उठी।

और देवर्षि नारद वहीं बैठकर तपस्या में लीन हो गए।इन्द्र नारद की तपस्या से घबरा गए।उन्हें हमेशा की तरह अपना सिंहासन व स्वर्ग खोने का डर सताने लगा।इसलिए इन्द्र ने नारद की तपस्या भंग करने के लिए कामदेव को उनके पास भेज दिया।वहां पहुंच कामदेव ने अपनी माया से वसंतऋतु को उत्पन्न कर दिया।

पेड़ और पौधों पर रंग बिरंगे फूल खिल गए और कोयलें कूकने लगी,पक्षी चहकने लगे।शीतल,मंद,सुगंधित और सुहावनी हवा चलने लगी।रंभा आदि अप्सराएं नाचने लगीं ।किन्तु कामदेव की किसी भी माया का नारद पे कोई प्रभाव नहीं पड़ा।तब कामदेव को डर सताने लगा कि कहीं नारद क्रोध में आकर मुझे श्राप न देदें।

जैसे ही नारद ने अपनी आंखें खोली, उसी क्षण कामदेव ने उनसे क्षमा मांगी।नारद मुनि को तनिक भी क्रोध नहीं आया और उन्होने शीघ्र ही कामदेव को क्षमा कर दिया।कामदेव प्रसन्न होकर वहां से चले गए।कामदेव के चले जाने पर देवर्षि के मन में अहंकार आ गया कि मैने कामदेव को हरा दिया।

नारद फिर कैलाश जा पहुंचे और शिवजी को अपनी विजयगाथा सुनाई।शिव समझ गए कि नारद अहंकारी हो गए हैं और अगर ये बात विष्णु जी जान गए तो नारद के लिए अच्छा नहीं होगा।ये सोचकर शिवजी ने नारद को भगवन विष्णु को ये बात बताने के लीए मना किया। परंतु नारद जी को ये बात उचित नहीं लगी।

(MAJOR NOTE) The reason Manafort took over Trump's campaign in just 3 weeks despite being hired as a mere delegate counter is that ROGER STONE initiated a whisper campaign against Corey Lewandowski. That campaign started...days after Manafort returned from meeting with WikiLeaks.

2/ Stone and Manafort are longtime business partners; we'd expect Manafort to tell Stone what Assange told him, and for him to expect Stone to then pressure the campaign not only to hire him but quickly elevate him.

And who was Stone in touch with at the campaign? Trump himself.

3/ I bet you Manafort's late March emails are very interesting and that some of them are to/from his longtime business associate Roger Stone. And I bet Roger Stone's late March phone calls are very interesting and that some of them are to/from his longtime friend Donald Trump.

4/ At the end of March 2016, Trump suddenly convenes a NatSec meeting. At that meeting he directs his NatSec team to change the RNC platform in July to benefit Putin. Who later takes credit for that change?

Paul Manafort's business associate, Kremlin agent Konstantin Kilimnick.

5/ Why did Donald Trump suddenly convene a NatSec meeting 3 days after he finally hired Manafort? Why did he issue a pro-Kremlin directive at that meeting that Manafort's camp would later take credit for? What did Manafort tell Trump in the days leading up to that NatSec meeting?

List of companies with more than 50% Market Share:

1) Maruti Suzuki (passenger cars)
2) APL Apollo (structural & pre galvanized tubes)
3) CDSL (investors accounts)
4) Interglobe Aviation (air traffic passengers)
5) GMM Pfaudler (glass lined equipment)

6) Asian Paints (decorative paints)
7) Colgate (oral care)
8) Symphony (coolers)
9) PGHH (female care & vaporub)
10) La Opala Rg (opalware)
11) HLE Glasscoat (filtration & drying equipment)
12) Delta Corp (online poker games)
13) Bajaj Auto (3W segment)
14) Vinati Organics (IBB)

15) OCCL (insoluble sulphur)
16) LMW (textile machinery)
17) Bajaj Consumer (almond hair oil)
18) Indiamart Intermesh (online B2B Classified space)
19) Vst Tillers (power tillers)
20) Sanghvi Movers (overall domestic crane hiring market)
21) Emami (antiseptic & male grooming)

List of companies with more than 60% Market Share:

1) Concor (domestic container cargo transport)
2) Exide (lead batteries)
3) Naukri (Indian job market space)
4) Praj (ethanol plant installing)
5) ACE (mobile & tower cranes)
6) Pidilite (adhesives)
7) Jamna Auto (leaf spring)

8) CAMS (RTA within mutual fund industry)
9) Time Technoplast (polymer based industrial packaging)

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.