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RISHI PINGALA:THE ANCIENT INDIAN SAGE DISCREDITED FOR HIS MAJOR CONTRIBUTIONS TO MATHEMATICS & PHI

Have you ever heard about,"The Golden Ratio"?
The Golden Ratio or Phi(Greek) is a mathematical ratio of nature. Mother Nature is the most exquisite artist in herself.

&Its this Phi she uses to create all the beauty in World.
If anyone uses this ratio correctly in their art,it can turn into a spell-binding piece of work.Such is the significance of'Phi'.But my thread isn't about it.Its about somethin called'Fibonacci Series'&who actually made it

Phi is related to Fibonacci series(0,1,1,2,3,5,8,13,21,34...)
&this series is responsible for everything dat is beautiful on this planet. Just try to calculate the ratio of any 2 successive numbers ÷ each of them by the number before it in above infinite series.

E.g. 3/2=1.5, 5/3=1.666, 8/5=1.6, 34/21=1.61904 and so on. Did you notice something? The ratio is coming almost same every time. This ratio is called 'Phi 'which is numerically represented as 1:1.618. This Golden Spiral is made in exact proportion of 1:1.618.

The thing I want to emphasize here is that we always find only those things appealing and attractive that have Phi. It's in our subconscious. Isn't it some revelation? We are the part of this Divine proportion of the God or the Mother Nature. Check these:

🚢 Atomic Essays Republishing Framework 🚢

1. Write Atomic Essay
2. Post image on Twitter
3. (Bonus) Copy/paste text as thread
4. Find relevant Question on Quora. Copy/paste Atomic Essay + image.
5. Copy/paste again on Medium.
6. Again on LinkedIn
7. Win

Guide below ✍️🚢👇

Step 1: Write Atomic Essay

I really enjoy writing right inside the Figma template. It helps give me a good sense of exactly how much "real estate" I have/have left before my time us up and I'm out of space.

Once finished, I export the image and send to my phone on Slack.

Step 2: Post image on Twitter

Before I publish my Atomic Essay on Twitter, I use the Edit/Photo Markup function on the iPhone to highlight standout sentences.

These are usually power-phrases: things the reader skims and thinks, "That's interesting," prompting them to read more.

Step 3: For the Ship 30 for 30 challenge, I only turn Atomic Essays into threads if...

1. The piece is SUPER actionable and lends itself well to the thread format (lots of bullets, quick points, lists, etc.)

or...

2. I'm referencing research and want to link to the pieces.

Step 4: Find relevant Question on Quora. Copy/paste Atomic Essay + image.

Search on Quora around the topic you wrote about. Find a related Question. Copy/paste the whole Atomic Essay as your "answer" (& fix formatting). Then include image so it appears in the thumbnail.

Can we control the output of neural generation systems?
Can we build a better chatbot using this control?
And what makes a good conversation anyway?

Check out the blog post for our NAACL paper:

https://t.co/UAAUxSeA0L

#AI #NLProc #DeepLearning #ConversationalAI @facebookai

This post got a little delayed since the paper's publication, but the intervening time has given me an opportunity to reflect.

So this post has some extra thoughts regarding more recent NLG developments - e.g., pretrained models like GPT(2), and the suitability of beam search.

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.

NESARA / GESARA explained for all who are not aware.

"The End of Poverty, The End of Debt, The Beginning of a New Golden Age!"

No Poverty, No Hunger, No Debt, Only Global Prosperity and Peace for ALL

NESARA will merge with GESARA

N = National (US)
G = Global