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RANI NAIKI DEVI AND THE BATTLE OF KASAHRADA

Mohammad Ghori, the young Ghurid Prince conquered Ghaznavids in Afghanistan in the year 1173 CE. He conducted some successful raids into the Indian territory, which even Mahmud Ghazni & the Persians couldn't do.

First&foremost,Ghori invaded states of Multan &Uch in India.After this he turned to southern states of Gujarat &Rajputana.Everybody knows this about Ghori,but what most pple r unaware of is the defeat he suffered at hands of Chalukyan Queen Naiki Devi in 1178 Battle of Kasahrada.

When he marched southwards, his first target was the prosperous fortified town of Anhilwara Patan.
It was the capital of Chalukyas(also known as Solankis of Gujarat) which was established by Vanraj of Chapotkata Dynasty in 8th century.

American historian Tertius Chandler states that it was the 10th largest city in the world at that time. The history of Chalukyas has been written by many historians, but the valour of the Great Queen Naiki Devi remains unsung.

Born to the Kadamba King Paramardin of Goa,Naiki Devi was well trained in sword fighting, horse riding, strategy & every other subject of Statesmanship. She was married to King Ajaypala of Gujarat who sat on throne in 1171CE. Unfortunately,the King died within 4 yrs of his rule.

🕵️‍♂️ How Google's PageRank algorithm works

The PageRank algorithm gives each page a rating of its
importance, which is a recursively defined measure of importance, based on if important pages link to it.
It's recursive because the importance of a page refers back to the importance of other pages that link to it

Here's how it works in practice:
1⃣ We start with some pages and crawl them for links

2⃣ Each page has 1/N points (where N as the total number of pages)

3⃣ Add points to each page for the amount of links to it, divided by the number
of links emanating from the sources of these links

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.