HAVELLS

Double Top Buy above 1485.56 daily close on 1% Box Size chart. https://t.co/zY8JwRIZUu

SRI MOOKAMBIKA DEVI, KOLLUR (KAR)

There was an Asura, Kaumasura, who was doing penance to please Shiva and get a boon. To prevent him from asking for a boon, Devi Saraswati made him dumb (hence called Mookasur)
Later, Adi Parashakti slayed him and came to be called Mookambika.

It is said that Adi Shankaracharya had a vision in which Devi agreed to follow him provided he did not look back. He kept walking and was assured of her presence due to the sound of anklets. When he reached here in Kollur, he turned back as the sound of anklets had stopped.

So Devi stayed here and merged with the lingam. The place came to be called Mookambika Kshetram.
The linga has integrated on it’s left side MahaKali, Maha Lakshmi and Maha Saraswathi and on the right side- Brahma, Vishnu and Shiva.

A swarna rekha (gold line) divides this linga into left and right portion. Adi Shakthi in this Udhbhavlinga form appears only here.
Devi sits in padmasana and has 4 hands.
Adi Shankaracharya installed the Sri Chakra in front of Devi and also composed the Soundarya Lahiri here.

THE SCIENCE BEHIND YAJNA

Fire is, of course, one of the greatest discovery of mankind. Our control over the fire dramatically changed the entire human habits; earlier it was used to cook food, protection from animals and to stay warm.

Among the many applications of fire, one is Yajna (यज्ञ, yajña); also called as Havana (हवन) or Agnihotra (अग्निहोत्र).
Yajna is elaborately described in Yajurveda. In Yajurveda, Yajna is the greatest among all works (Karman). One can purify his /her soul by performing Yajna.

In Vedic culture, it is thought that the holy fire is the mediator between Gods and us; when the offerings are made to Yajna with chanting mantras, it directly reaches the Gods, who bless the people with rains, food, health, and good luck.

According to Apastamba Sutra, the definition of Yajna is Vedic sacrifice; an act by which we surrender something for gods e.g. grains (Anna), pulse, milk and milk product (ghee, butter, curd) fruits, plant juice (soma), etc.

As we said; Yajna is the contact medium for gods and

to seek their blessings.

There are many types of Yajna performed by Hindus for e.g.,

1. Rajsuya Yajna
2. Ashwamedha Yajna
3. Chaaturmasya Yajna
4. Vaajpey Yajna
5. Purushmedha Yajna
6. Sarvamedha Yajna

Pyramiding 2.0

A thread-

What is pyramiding?

It is simply adding to your winners.

Why use pyramiding?

1. It makes your winners bigger than your losers.

• Because you are adding positions when your trades are going your way, you are increasing your gains but your losers will be small comparatively.

2. You lose less during Draw-down time ( it saves you from losing big during whipsaws )

• Many times when the market is in sideways mode, Entering a trade with full risk might not be the best idea.

• So, you test the waters with a small position first, it could be ⅓ or ¼ of your total risk.

• So if the Breakout fails or whipsaws come, then you will only lose a small part of your overall risk.

• Then when position starts to go in your favor you can add to it.

3. It helps you to concentrate on a few trades,

They say that don’t put all your eggs in one basket, but don’t put them in too many either.

• See, Everyone can see charts and find breakouts, pullbacks etc.

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.