#Ramayana #LecturesOnTheRamayana

Today, I will share a very touching, quite surprising unconventional thought process from Ramayana.

When Lakshmana erected the hut in Panchavati, Rama said something which is quite strange & unexpected. Thread on this incident below:

1/

🌺कैसे बने गरुड़ भगवान विष्णु के वाहन और क्यों दो भागों में फटी होती है नागों की जिह्वा🌺

महर्षि कश्यप की तेरह पत्नियां थीं।लेकिन विनता व कद्रु नामक अपनी दो पत्नियों से उन्हे विशेष लगाव था।एक दिन महर्षि आनन्दभाव में बैठे थे कि तभी वे दोनों उनके समीप आकर उनके पैर दबाने लगी।

प्रसन्न होकर महर्षि कश्यप बोले,"मुझे तुम दोनों से विशेष लगाव है, इसलिए यदि तुम्हारी कोई विशेष इच्छा हो तो मुझे बताओ। मैं उसे अवश्य पूरा करूंगा ।"

कद्रू बोली,"स्वामी! मेरी इच्छा है कि मैं हज़ार पुत्रों की मां बनूंगी।"
विनता बोली,"स्वामी! मुझे केवल एक पुत्र की मां बनना है जो इतना बलवान हो की कद्रू के हज़ार पुत्रों पर भारी पड़े।"
महर्षि बोले,"शीघ्र ही मैं यज्ञ करूंगा और यज्ञ के उपरांत तुम दोनो की इच्छाएं अवश्य पूर्ण होंगी"।

महर्षि ने यज्ञ किया,विनता व कद्रू को आशीर्वाद देकर तपस्या करने चले गए। कुछ काल पश्चात कद्रू ने हज़ार अंडों से काले सर्पों को जन्म दिया व विनता ने एक अंडे से तेजस्वी बालक को जन्म दिया जिसका नाम गरूड़ रखा।जैसे जैसे समय बीता गरुड़ बलवान होता गया और कद्रू के पुत्रों पर भारी पड़ने लगा

परिणामस्वरूप दिन प्रतिदिन कद्रू व विनता के सम्बंधों में कटुता बढ़ती गयी।एकदिन जब दोनो भ्रमण कर रहीं थी तब कद्रू ने दूर खड़े सफेद घोड़े को देख कर कहा,"बता सकती हो विनता!दूर खड़ा वो घोड़ा किस रंग का है?"
विनता बोली,"सफेद रंग का"।
तो कद्रू बोली,"शर्त लगाती हो? इसकी पूँछ तो काली है"।

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.

Remember: Just because an idea is old doesn't mean it's good.

Knowing whether old ideas are more likely to be good requires understanding whether conditions have changed in important ways.

Horse archery was an amazing method of warfare for over a thousand years...then people invented guns, and suddenly this great idea that had stood the test of time became obsolete.

Then again, when underlying conditions don't change much, tried-and-true approaches are probably better.

"Learning the lessons of history" usually just means assuming ergodicity and stationarity in an informal time-series model...

(end)

1. I was walking in the woods yesterday and I had an epiphany. It wasn’t one I wanted to have. I was thinking about today’s class, where we are reading Kimmerer’s ‘Gathering Moss’. I’ve taught Braiding Sweetgrass in multiple classes every year since 2016. It’s eminently teachable

2. Both books are beautifully written. Students uniformly enjoy them as assigned reading. They do a great job communicating to non-Indigenous folks the contours of basic Indigenous relationships to more-than-human beings. They’re wildly popular with white readers.

3. I’ve found Braiding Sweetgrass to be a great entry point for non-Indigenous folks to Indigenous scholarship. But this is where yesterday’s epiphany comes in. Neither Braiding Sweetgrass or Gathering Moss tends to the deep deep lineages of Indigenous scholarship in Canada or US

4. As the popularity of ‘Braiding Sweetgrass’ has soared, I’ve seen more and more white folks wax on about ‘braiding knowledge’. But they often don’t know any of the lineages of these concepts beyond what is presented in this one book. They confidently run with this.

5. Kimmerer does a commendable job of weaving in her own experiences, her family stories+nods to some very important knowledge keepers who deserve to be celebrated for their work. But Braiding Sweetgrass does not tend to the citational politics Sara Ahmed & others teach us about