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Some thoughts on episode 53 of #Hercai

Love,that is not real,has no passion. Its fire turns into embers, it doesn’t not smell. The day, the night, the moon&year burn without sun, light, comfort&sleep.
Ömer Hayyam

#Reyyan #Miran #ReyMir #EbruŞahin #AkınAkınözü
#HercaiThoughts

1
PRAISE
The praise in this episode goes foremost to Ebru because she portrayed Reyyan so realistically like a woman who truly lost her unborn child. I was in awe and pain watching her along the episode, all her breakdowns and tears were so real.
#Hercai

2
Of course also Akın took his part and did his part masterfully, but he rightfully was the one who stood aside in this specific episode that needed to be about Reyyan’s pain and thus concentrated on Ebru.
#Hercai

3
And last but not least, if I say thank you to something that B gave us this year, then it’s to have Ali as our director. Also in this episode we saw the artistic eye behind every shot and I loved it.
#Hercai

4
OVERALL COMMENT
I don’t know who wrote ep. 52 but ep. 53 was going strong once more on symbols: roses, stones, fire and trees were incorporated and especially the ReyMir dialogues were written very well. I truly loved every ReyMir scene.
So let's get into it.
#Hercai

💬 I often get Q’s like these:

How did you get your first customers for WIP? How did you grow BetaList’s traffic? Etc.

Makers are looking to reverse-engineer success. I see it everywhere.

I don’t think it works that way and the answers to those questions are mostly useless. 💥

I have built dozens of different products over the last couple of years. The vast majority failed. 😭

Surely if I know the answers to these questions, but still fail over and over again, these answers aren’t that useful. 🤷‍♀️

So what’s a better question to ask? 🤔

99.9% of the questions I receive are about the products that did well. In a way that makes sense, because we quickly forget about those that didn’t succeed.

🧠 This is known as survivorship bias.

Focusing on what survived, while ignoring what made it survive in the first place.

The real question, what you really want to know, is this:

What makes @WIP, @BetaList, and to some extent @AllStartupJobs succeed where my countless other attempts failed?

What separates a failed product 👎 from a successful product 👍?

Honestly, I don’t know. I wish I did.

It’s like Steve Jobs said “I’ll know it when I see it.” 👀

Same is true when we make products. We don’t know upfront what will work. But once we see an inkling of a product that does have potential, it’s not that hard to spot.

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.