I discovered a fantastic GitHub repository called: Projects

This repository contains about 125 project ideas from beginner to intermediate level that anyone can solve in any programming language.

Link - https://t.co/Arl5fl1lQI

ugh i wanted to do a thread about the wars in Star Wars and how the two outlying wars (the clone wars in the prequels and the cold war/civil war in the sequels) are really weird but i don't feel like it any more.

my first argument is that the Clone Wars were a really complex morally gray moment in Star Wars canon in like, 5 different directions and I'd kind of like to know why Lucas did that.

especially with the direct line that's drawn in the OT with the allegory of WW2 and Vietnam with the rebels being the Vietnamese and the Empire being America. That...doesn't exist in the Clone Wars.

for starters: it's weird it's called the Clone Wars. I get why, because that's what Kenobi calls it in STAR WARS, but the clones are a small facet in the larger story of the war.

the clones basically only exist as a macguffin in AOTC that is...extremely confusing ultimately. Because if you accept that Sidious has this grand master plan to use this war to destabilize the Republic and form an Empire...it's way too complex.

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.

Sri Parasurameswara Swamy Mandir, Gudimallam, Andhrapradesh

Gudimallam is a small village located in Chittoor district. It is seven miles away from Tirupati. This small village came to significance,due to it's very beautiful Shiva Mandir with history of thousands of years. 1/n

The main importance of the mandir lies in the Linga which is housed in the Garbhagriha of the temple & considered as the only one of such type. It is the earliest Linga discovered so far and it has been assigned to the 3rd century BCE.(Satavahana period) 2/n

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.