Thread: ARE YOU PREPARED TO LOSE EVERYTHING?
Because we are looking at the edge of an ABYSS right now.
President Trump has MASTERFULLY taken us to the cusp of unrecoverable DISASTER, a plunge that, if taken, would be fatal to our Republic & bring misery & suffering Worldwide.

॥ॐ॥
अस्य श्री गायत्री ध्यान श्लोक:
(gAyatri dhyAna shlOka)
• This shloka to meditate personified form of वेदमाता गायत्री was given by Bhagwaan Brahma to Sage yAgnavalkya (याज्ञवल्क्य).

• 14th shloka of गायत्री कवचम् which is taken from वशिष्ठ संहिता, goes as follows..

• मुक्ता-विद्रुम-हेम-नील धवलच्छायैर्मुखस्त्रीक्षणै:।
muktA vidruma hEma nIla dhavalachhAyaiH mukhaistrlkShaNaiH.

• युक्तामिन्दुकला-निबद्धमुकुटां तत्वार्थवर्णात्मिकाम्॥
yuktAmindukalA nibaddha makutAm tatvArtha varNAtmikam.

• गायत्रीं वरदाभयाङ्कुश कशां शुभ्रं कपालं गदाम्।
gAyatrIm vardAbhayANkusha kashAm shubhram kapAlam gadAm.

• शंखं चक्रमथारविन्दयुगलं हस्तैर्वहन्ती भजै॥
shankham chakramathArvinda yugalam hastairvahantIm bhajE.

This shloka describes the form of वेदमाता गायत्री.

• It says, "She has five faces which shine with the colours of a Pearl 'मुक्ता', Coral 'विद्रुम', Gold 'हेम्', Sapphire 'नील्', & a Diamond 'धवलम्'.

• These five faces are symbolic of the five primordial elements called पञ्चमहाभूत:' which makes up the entire existence.

• These are the elements of SPACE, FIRE, WIND, EARTH & WATER.

• All these five faces shine with three eyes 'त्रिक्षणै:'.

SIRSI MARIKAMBA DEVI, UTTAR KANNADA DIST. (KAR)
#bharatmandir

Also known as Doddamma (elder sister of all Mariammas). Marikamba of Sirsi is both Mari (terror) as well as Maremma (savior). She is terror in anger & Mother in love. That is why she is called Marikamba.

There was a man by the name of Asadi/ Basava. He used to attend a fair at the neighboring village. Once, he was harassed by some men due to his poor status. So he did not go to the fair. Instead, he stayed in his village Sirsi and prayed to Devi.

One night, he had a dream where Devi Kali informed him about her presence in a tank at his village. When the tank was searched, people found a box with parts of murti. The different parts were assembled together and since then Sirsi Devi became popular in the region.

The kshetram has extremely striking murals in ‘Kaavi’ art. It is an art form of the Konkan region of coastal Karnataka. This stunning art involves creation of patterns in red colour against a white background.
Marikamba rathyatra is one of the biggest Car festivals in Karnataka.

What does Gold- Backed mean?
The Chinese Elders have been accumulating gold for hundreds of years. They are gold-rich. Some of their gold was loaned to the Central Banking System so that it could fund the rebuilding of Europe after the many war-torn years from World Wars I and II

BTW they never returned it but used it to start their financial empire to enslave humanity. This is the Central Bank Banking System./2

Maybe this is why one of the conditions to use their gold for the QFS is the demise and full disclosure of the Central Bank banking system before the RV can take place./3

The Elders have caves of hidden gold and are now using it to gold-back the currencies in the QFS for every country in the world. It is important to understand that the QFS and the money it is about to release is all brand-new currency./4

It was designed by the Alliance to fund the new financial system that will reset all Currencies of the world to Gold-Backed currency./5

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.