Hospitals where Greenfield happens within the same city are the second best place to look for when the first idea of just brownfield capex isn't there. Soic students will relate more :)

For fun I made a Robinhood account. It took me 5 minutes to sign up, get approved and transfer funds for trading...

Here's some details... Read on.

...it took me another 5 minutes to get qualified for options.

If I share a sign up I get "free shares" and I can share as much as I want up to 500$ worth.

The main UI has a prepopulated "list" of stocks/crypto to watch and links to news.

Clicking a stock opens a "trade" button that looks like this.

Notice options is first, buy second.

Options took me to an approval process (which took about a minute) and now away I go trading Baba options.

Buying stock looks like this. It emphasizes fractional shares.

The Straddle is my favorite trade these days. But more often than not, one needs to adjust to make a good return from it. There are many ways to do this depending on your view, psychology and imagination. Will discuss a few that I use

The Straddle is a delta neutral strategy and it is important to keep deltas in check during the course of the Trade and not let them run away from you. Of course Volatility is the elephant in the room- but not going there as that will require a separate discussion

The first and most common method is to go 'inverted.' That is to say to shift the untested side to generate additional Premium and increase the runway to Breakeven. So if you start with a Straddle at 700 and price goes to 740, you can move the PE up to say 720.

How much should you move the untested side? While again this is a matter of skill, experience and artistry, the rule of thumb is to cut deltas by half. Remember, prices will move and you have to allow sufficient room for them to fluctuate

The second method is to shift the entire Straddle in the direction of the move. So if you have a 700 Straddle and Price goes to 740, you can shift the Straddle to 720. If you time this right, it can generally be done at virtually no cost or minimal cost.

The Chanting of following names of Bhagwan Vishnu, immensely helps us in overcoming the obstacles in our daily life.
These names are from Sri Vishnu Sahasranama!
|. "Om Vashatkaaraaya Namaha" : For Success in Business.
||. "Om Aksharaaya Namaha" : For Success in Studies.

|||. "Om Bhuthabhavanaya Namaha" : For Good Health.
|V. "Om Paramaathmane Namaha" : For Self Confidence.

Src: VAK magazine from Chilkur Balaji Temple, Hyderabad - Sri @csranga
#SanatanaDharma #SanatanaSanskriti #Sattology

One needs to chant the following slokas 28 times to get rid of certain problems in life.
Om Hrushikesaya Namah - For Overcoming Bad habits
Om Vashatkaaraya Namah - For Success in Business, Interviews, visa interviews, building relationships

Om Srimate Namah - For Handsome appearance & wealth
Om Aksharaya Namah - For Education & better financial strength
Om Paramatmane Namah - For self employed people, for promotions and success in games.
Om Putatmane Namah - To remove mental stress & for mental peace

Om Sarmane Namah - For Job Satisfaction
Om Bhutadaye Namah -To amend soured friendship or any personal relationship
Om Dhatre Namah - For issueless couple
Om vidhatre Namah - Pregnant Ladies to Chant for healthy babies.
Chant the following 108 times :

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.