Out of 4 stocks

1st 3 likely contribute to index

One may under perform (wipro)

Checking #Probability

In 1937, a trucker got frustrated waiting for his cargo to be unloaded. He later sold his company for $1.1 billion.

Here’s the story of how Malcom McLean made a billion dollars and which startup could be following in his footsteps. (Inspired by our episode with @laurabehrenswu)

In the early 1950s, the shipping industry was a dying business.

New York’s docks were handling half as much domestic cargo in the early 1950s as they had been in the depressed 1930s.

In thirty years, no one had invested significant money in coastal shipping.

Why?

As McLean found out, loading ships was difficult and slow.

A truck or train would deliver items to the port. Each item was unloaded separately, recorded, and carried to storage.

When a ship was ready, each item was taken from storage, counted again, and hauled on-board.

The cost of this labor was significant. As one expert at the time explained:

“A four-thousand-mile voyage for a shipment might consume 50 percent of its costs in covering just the two ten-mile movements through two ports.”

Yet, despite the industry’s issues, it felt little pressure to change.

Foreigners were barred from operating domestically. Cartels ran international routes. And gov. subsidies eased the pain of labor costs.

Reshaping the industry needed an outsider.

Enter Malcolm McLean.

BANASHANKARI AMMA, NEAR BADAMI (KAR)
#bharatmandir #navratri2021

Originally built by Chalukyas in 7th century. Devi Banashankari was their family deity. This image of Banashankari Amma was installed by Jagadekamalla I in 630 AD.

Later renovations were done by Marathas.

Once, a demon Durgamasur was troubling the people of this area. Answering the prayers of Devas, Ma Parvati emerged from the yajna fire as Banashankari and killed Durgamasur.
This area has rich forests of coconut, betel nut and other trees.
@GunduHuDuGa

It is said that during a severe famine, Devi provided vegetables and fruits to the people of this area.
Hence, she is also worshipped here as Shakambhari.
In the annual festival called Banashankari jatre, a Rath yatra takes place and Devi moves around the city in the chariot.

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.

प्राचीन काल में गाधि नामक एक राजा थे।उनकी सत्यवती नाम की एक पुत्री थी।राजा गाधि ने अपनी पुत्री का विवाह महर्षि भृगु के पुत्र से करवा दिया।महर्षि भृगु इस विवाह से बहुत प्रसन्न हुए और उन्होने अपनी पुत्रवधु को आशीर्वाद देकर उसे कोई भी वर मांगने को कहा।

सत्यवती ने महर्षि भृगु से अपने तथा अपनी माता के लिए पुत्र का वरदान मांगा।ये जानकर महर्षि भृगु ने यज्ञ किया और तत्पश्चात सत्यवती और उसकी माता को अलग-अलग प्रकार के दो चरू (यज्ञ के लिए पकाया हुआ अन्न) दिए और कहा कि ऋतु स्नान के बाद तुम्हारी माता पुत्र की इच्छा लेकर पीपल का आलिंगन...

...करें और तुम भी पुत्र की इच्छा लेकर गूलर वृक्ष का आलिंगन करना। आलिंगन करने के बाद चरू का सेवन करना, इससे तुम दोनो को पुत्र प्राप्ति होगी।परंतु मां बेटी के चरू आपस में बदल जाते हैं और ये महर्षि भृगु अपनी दिव्य दृष्टि से देख लेते हैं।

भृगु ऋषि सत्यवती से कहते हैं,"पुत्री तुम्हारा और तुम्हारी माता ने एक दुसरे के चरू खा लिए हैं।इस कारण तुम्हारा पुत्र ब्राह्मण होते हुए भी क्षत्रिय सा आचरण करेगा और तुम्हारी माता का पुत्र क्षत्रिय होकर भी ब्राह्मण सा आचरण करेगा।"
इस पर सत्यवती ने भृगु ऋषि से बड़ी विनती की।

सत्यवती ने कहा,"मुझे आशीर्वाद दें कि मेरा पुत्र ब्राह्मण सा ही आचरण करे।"तब महर्षि ने उसे ये आशीर्वाद दे दिया कि उसका पुत्र ब्राह्मण सा ही आचरण करेगा किन्तु उसका पौत्र क्षत्रियों सा व्यवहार करेगा। सत्यवती का एक पुत्र हुआ जिसका नाम जम्दाग्नि था जो सप्त ऋषियों में से एक हैं।