Laptop purchase decision

So in the past, I’ve ranted about the “confusion marketing” in the laptop market. (see here).

So this time around after more than 5 years, when i had to buy a new laptop, I decide to apply some analytical ideas, i’ve picked up over this time working.

So I created this sheet, which helped me out.

Since i had written a series of posts in the past about different types of mean, i had created this blog post.
So I ended up buying this.

Yentl Syndrome: A Deadly Data Bias Against Women

Longreads

Caroline Criado Perez | An excerpt adapted from Invisible Women: Data Bias in a World Designed for Men | Harry N. Abrams | 22 minutes (5,929 words)

In the 1983 film Yentl, Barbra Streisand plays a young Jewish woman in Poland who pretends to be a man in order to receive an education. The film’s premise has made its way into medical lore as “Yentl syndrome,” which describes the phenomenon whereby women are misdiagnosed and poorly treated unless their symptoms or diseases conform to that of men. Sometimes, Yentl syndrome can prove fatal.

If I were to ask you to picture someone in the throes of a heart attack, you most likely would think of a man in his late middle age, possibly overweight, clutching at his heart in agony. That’s certainly what a Google image search offers up. You’re unlikely to think of a woman: heart disease is…

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Aruvi(2017) — review

Movie: Aruvi

Brilliance of screenplay irony:

  • — The scene at the shooting set after the shooting, fights with that cheater, exploitative guy.
    The irony arises from the numbness/indifference of the actress, and the way she makes puppets out
    of the shooting crew, which for a change have to face real life-or-death drama rather than the
    made-up things they shoot. Not to mention the background bass.

  • — The idealist wannabe director’s impractical dream story to direct.

  • — The role reversal of the prima donna actress serving tea.

  • — Ofcourse, the mock-tv show, of the actress.

Some things about the movie that left me cold though
* — The manner how the protagonist got HIV infection is a rather low probabilty and rare method(it had to go from the live blood of coconut seller to, any bleeding gums in the protagonist mouth or other bleeding wounds in the digestive tract), and the director had to reach for it to avoid the pre-marital sex taboo culture. I personally think it is a meaningless one to stick to. (Not that i suggest we let US style marketing and companies use dating and sex as a lure. Ironically, sticking to cultural excuses would drive some youth to that approach only. )

I’m not giving up!”  I raised my voice, angry, surprised at myself for being angry.  I took a breath, forced myself to return to a normal volume, “I’m saying there’s probably no fucking way I’ll understand why she did what she did.  So why waste my time and energy dwelling on it?  Fuck her, she doesn’t deserve the amount of attention I’ve been paying her. I’m… reprioritizing.”

She’s a bully,” I said.  “At the end of the day, she only wants to fight opponents she knows she can beat.”

“I’ve fought two Endbringers,” Shadow Stalker said, stabbing a finger in my direction.  “I know what you’re trying to do.  Fucking manipulating me, getting me into a dangerous situation where you’ll get me killed.  Fuck you.”

Gaussian Mixture Models.. GMMs

Gaussian Mixture Models

  • A probabilistic model
  • Assumes all data points are generated from a mixture of finite no. of gaussian
    distributions
  • The parameters of the gaussian distributions are unknown.
  • It is a way of generalizing k-mean(or k-medoid or k-mode for that matter) clustering to use the
    co-variance structure/stats as well as the mean/central-tendency measures of latent
    gaussians.

scikit-learn

Pros:

  • Fastest for learning mixture models
  • No bias of means towards zero, or bias cluster sizes to have specific structures

Cons:

  • When there’s not enough points per mixture, estimating covariance matrices becomes
    difficult
  • Number of components; will always use all the components it has access to, so might need
    missing or test-reserved data..

  • No. of components can be chosen based on BIC criterion.

  • Variational Bayesian Gaussian mixture avoids having to specify number of components

Variational Bayesian Gaussian Mixture

Fitting a Gaussian model to data

Share: Worm

have been doing this for ten years.  I admire you for retaining your…” he trailed off.

“Idealism?”

“Not a word I’m familiar with, Weaver.  Faith?”

“Faith works.”

“I have none left, after ten years.  No faith.  We are a wretched, petty species, and we have been given power to destroy ourselves with