Boomers have permanently crippled /pol/.
It's threads on the RV are all taken over by boomer Q posting and babies first conspiracy posts.

दधीचि ऋषि को मनाही थी कि वह अश्विनी कुमारों को किसी भी अवस्था में ब्रह्मविद्या का उपदेश नहीं दें। ये आदेश देवराज इन्द्र का था।वह नहीं चाहते थे कि उनके सिंहासन को प्रत्यक्ष या परोक्ष रुप से कोई भी खतरा हो।मगर जब अश्विनी कुमारों ने सहृदय प्रार्थना की तो महर्षि सहर्ष मान गए।

और उन्होनें ब्रह्मविद्या का ज्ञान अश्विनि कुमारों को दे दिया। गुप्तचरों के माध्यम से जब खबर इन्द्रदेव तक पहुंची तो वे क्रोध में खड़ग ले कर गए और महर्षि दधीचि का सर धड़ से अलग कर दिया।मगर अश्विनी कुमार भी कहां चुप बैठने वाले थे।उन्होने तुरंत एक अश्व का सिर महर्षि के धड़ पे...

...प्रत्यारोपित कर उन्हें जीवित रख लिया।उस दिन के पश्चात महर्षि दधीचि अश्वशिरा भी कहलाए जाने लगे।अब आगे सुनिये की किस प्रकार महर्षि दधीचि का सर काटने वाले इन्द्र कैसे अपनी रक्षा हेतु उनके आगे गिड़गिड़ाए ।

एक बार देवराज इन्द्र अपनी सभा में बैठे थे, तो उन्हे खुद पर अभिमान हो आया।

वे सोचने लगे कि हम तीनों लोकों के स्वामी हैं। ब्राह्मण हमें यज्ञ में आहुति देते हैं और हमारी उपासना करते हैं। फिर हम सामान्य ब्राह्मण बृहस्पति से क्यों डरते हैं ?उनके आने पर क्यों खड़े हो जाते हैं?वे तो हमारी जीविका से पलते हैं। देवर्षि बृहस्पति देवताओं के गुरु थे।

अभिमान के कारण ऋषि बृहस्पति के पधारने पर न तो इन्द्र ही खड़े हुए और न ही अन्य देवों को खड़े होने दिया।देवगुरु बृहस्पति इन्द्र का ये कठोर दुर्व्यवहार देख कर चुप चाप वहां से लौट गए।कुछ देर पश्चात जब देवराज का मद उतरा तो उन्हे अपनी गलती का एहसास हुआ।

🕵️‍♂️ 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

10 Signs of Social Intelligence 🧠

1-Smile
2-Good posture
3-Strong eye contact
4-Fluid hand gestures
5-Let’s others talk more
6-Listens & relates
7-Comfortable Touching people
8-Walk with a swagger
9-Speaks at slower pace
10-Keeps calm under criticism

[ QUICK THREAD ]

1-Smile

How do you feel when you see someone that looks angry?

It’s a downer

No one will want to interact with you if you look pissed all of the time, it’s just negative energy

Plus, smiling is a sign of confidence and boosts others moods

2-Good posture

Don’t walk Into any social setting looking like the hunchback

It’s weak and makes you look low energy

Plus, you’re displaying low status with weak posture

Stand up straight with your chin up & chest out like a god

Walk like you’re proud of yourself

3- Strong eye contact

Ever shake someone’s hand & their eyes are either looking down or darting?

It’s weak & you probably won’t trust them

Or in conversation?

Eye contact while talking means you’re listening and paying *Attention*

Strong eye contact displays confidence

4-Fluid hand gestures

Use your hands to talk & express your points

Don’t stand there like a robot

Using the hands can make what you’re talking about exciting

It can also be used to emphasize

Exciting = positive emotion

Remember, making people FEEL is what’s important

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.