Where should I start?
1) Is Blockfi profitable?
2) How much money does BlockFi have on its balance sheet?
3) Is the yield that BlockFi pays to its depositors earned or subsidized by billionaire investors?
4) Has BlockFi ever been profitable?
5) Are you planning to go public?

Sri Maha Bhairavar Rudhra alayam, Chengalpet, Tamilnadu

This temple is being constructed merely on the instructions from Bhairava by his devotee Shri Bhairava Siddhantham Swamy. The Temple is fully enshrinedin a transparent glass enclosure..1/n

with Rudrakshas on both the sides of the entrance This temple has a circular Sanctum Sanctorum which has twelve stepseach representing a sign of zodiac and after that twelve steps we find the supreme deity of this temple Sri Kshetra Paata Bhairava. 2/n

Beneath the Bhairava, in the lower level are installed the Ashta Bhairavas, SapthaRishis in the form of stones, and Saptha Kannis in the form of brass lamps. Thereare a number of little blue and white lights in the ceiling that is shaped like a dome.. 3/n

which give an ethereal atmosphere when switched on. The steps representthe twelve signs of the zodiac and when one steps over them to reach thesanctum sanctorum all the doshas pertaining to the horoscope are cleared onceand for all..4/n

The Sthala Vriksham ”Peepal Tree” which is grown on a pedestal, a trident which is considered as Bhairavi, Pancha Mukha Anjaneya, Prathyankara Devi, Subramanya, Rudra Vinayaga, Vaishnavi Devi, Naga Rudra, Eswara, Vana Durga etc. 5/5

#IncredibleBharat
#DekhoApnaDesh

🕵️‍♂️ 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

(A THREAD)

18 Things I Would Do At 18:

I was a dumb 18-year-old. I thought I had the world figured out. I didn't.

I stayed up till the wee hours. Drank every liquor invented.

Partied non-stop. Woke up after noon.

Surfed the web aimlessly.

Read BS self-help.

Repeat.

If I could do it all over again, here's 18 things I would do...

PHASE ONE: GET MY MIND RIGHT

ACTION #1: Break the chains - I wish I made the decision earlier. I didn't. I was scared. Anxious. Nervous. I knew once I made the decision, there was no going back.

Don't be like me.

Make the decision at 18. The decision to pave your own path in life. To go against the herd. To choose your own adventure. To commit to whatever sets your heart on fire.

ACTION #2: Cut off every negative person in your life.

Scroll through the contacts in your phone...

Notice how the names make you feel.

Delete any and all names that fill you with doubt and negativity.

ACTION #3: Condition your mind to embrace the producer mindset instead of the consumer mindset.

Consume 20% of the time. Produce 80% of the time.

Case closed.

ACTION #4: Set an intention to make $10k a month and write it out every MORNING and NIGHT as if you already achieved it (in the past tense).

Visualize yourself already having the money as you write.

Cheesy?

Yes.

Does it work?

Yes.

PHASE TWO: EDUCATION

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.