I think a retest is pending
Should touch 250-273 zone before further rally 🤔

Bhoga Lakshmi Narasimha Swamy Temple
#Devarayanadurga
#Karnatakatemples 🚩

Bhoga Yoga Narasimha Swamy Temple, Devarayanadurga is located near Tumkur of Karnataka. There are two temples are here for Narasimha Swamy, one is at the base of the hill which is Bhoga Narasimhar and 👇

the other on the top of  hill which is Yoga Narasimhar.

Bhoga Narasimha swamy Temple Prathista is said to be done by Maharshi Durvasa who did tapasya here. While doing tapasya Muni wished to bath in river Ganga and decided to travel to Ganga river bank,

At that time Ganga Devi appeared here and said she will remain here in the temple pond.

Yoga Narasimha swamy Prathista is believed to be done by Bhrama Deva who did tapasya here for many years.

Narasimhar appeared with Lakshmi Devi here to give darshan to Bhrama Deva here.

उग्रं वीरं महाविष्णुं ज्वलन्तं सर्वतोमुखम्।
नृसिंहं भीषणं भद्रं मृत्युमृत्युं नमाम्यहम् ॥

Om Shri Narasimha Murthaye Namah
🙏🕉🙏

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.