You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

↓

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.

Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem → Potential Solution → Data.

For a long time, I didn't understand how to use Virtual Environments in Python 🐍.

If this is just, let's end it here and now: 🧵👇

[2] Virtual Environments let you deal with the dependencies that your code has with external Python libraries.

It avoids having conflicts when your projects depend on different versions of the same library.

👇

[3] Let's imagine that you are building your first Python project and you install the "requests" library:

pip install requests

You get version 2.24.0 installed in your system.

👇

[4] A month later, you decide to work on your second project. It also needs the "requests" library.

But the latest version is not 2.24.0 anymore.

Now version 3 is available, and that's the one you want to use!

👇

[5] You could upgrade your entire system to version 3, but then you'll be potentially breaking the first project you built that depends on 2.24.0!

Can you imagine this happening on a server with many more applications running?

👇

Do you know what's better than a machine learning model?

Two models.

More than one model working together to solve a problem is called "an emsemble." A simple way to build this is having each model vote for an answer.

But there's a problem with this approach: ↓

I'm gonna focus here on image classification.

Let's assume you built two different models:

• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)

They both solve the same problem, so you want to combine their results to pick the right answer.

The problem is that you have two models, so voting is not trivial.

What happens in this case?

• Model 1's answer: Class A
• Model 2's answer: Class B

Which one do you select?

Notice that this problem is not limited to an even number of models.

You could have 3 models, each giving you a different answer.

How do you decide which answer to choose?

There are multiple ways to approach this problem. I'll mention a few different ideas on this thread.

Important: Some of these ideas might not be feasible depending on your context. They have worked for me before on different situations, but every problem is different.

The two main machine learning techniques used in the industry today:

1. Gradient Boosted Trees
2. Deep Learning

Focus your time learning Scikit-Learn, XGBoost, and a Deep Learning library like Keras or PyTorch and you'll get the most for your time.

If you need to deal with structured data: Scikit-Learn + XGBoost.

If you need to deal with unstructured data (perceptual tasks): Keras or PyTorch.

Of course, there are many more techniques that will be helpful, but if you focus on these you'll be maximizing your ability to deliver value in the short term.

While #Tulane school of medicine is rightfully being exposed for its contemporary racism towards faculty, staff, students (and patients) due to the racist firing of Dr. Princess Dennar, it’s important to call out the deeply anti-Black history of this school, specifically... 1/

The way #Tulane became a private institution (originally was a public university) at the end of Reconstruction to avoid having to desegregate. The school charter says it was founded for “white men” only... but there’s more (much more) to this story... 2/

cw: medical racism

Tulane’s school of medicine rose to national prominence for its collection of human “specimens” in their anatomy museum “rivaled only by Harvard”. How did it acquire these “specimens” you might ask?

The collection was “curated” by a Dr. Edward Souchon... 3/

Dr. Souchon was mentored by the late James Marion Sims, the “father of modern gynecology”, who himself had started collecting body parts, organs, and skeletal remains from enslaved Black people during slavery. Souchon continued “curating” his own collection in horrific ways. 4/

The Souchon Collection grew over several decades, eventually touring the state as a traveling “public health” exhibit- nearly all of the body parts stolen, harvested and illicitly acquired from formerly enslaved and poor Black people via Charity Hospital. 5/

HOW TO LAST LONGER IN BED😋(18+)

Some men will last for hours while some men will have the audacity to last for 2mins. But don't worry, because you might even last for 8hrs in bed & she will end up cheating on you. A THREAD😂

Are you a virgin?

RULE 1: DO NOT judge yourself with just 1 round. That's pathetic of you.

Who knows, you might be Johnny sins or a fuck machine from your round 2

So I advice you masturbate your first round off before sex, or you allow your lady try this on you👇

(put your imaginations into work)
now let's get dirty....🤤

• hold his dick and rub it around your pussy, dip it in so that your pussy juice will wet it & get it ready.

All Women are at risk of getting Cervical Cancer. It often affect women over age 30.

Long-lasting infection with certain types of human papillomavirus (HPV) are the main cause of cervical cancer.

HPV is a virus that is passed from one person to another during sex.

A Thread 🚺

More than half of sexually active people might have HPV at some point in their lives, but few women will get CERVICAL CANCER.

Early Screening tests & the HPV vaccine can help prevent cervical cancer.

When cervical cancer is diagnosed early, it is highly treatable &....

2/cont.

associated with long survival and good quality of life.

Most (almost all) cervical cancers are caused by HPV.

There are many types of HPV.
Some HPV types can cause changes on a woman’s CERVIX that can lead to cervical cancer as time goes on, WHILE other types can cause genital

or skin WARTS . HPV is so common that most people get it at some time in their lives.

HPV usually causes no symptoms so you can’t tell that you have it. For most women, HPV might be SELF LIMITING (ie it will go away on its own); However, if it doesn't, there are a chances that

over time it may result to cervical cancer.

OTHER FACTORS CAN INCREASE YOUR RISK of CERVICAL CANCER —
•Having HIV/AIDS
•Smoking.
•Using Oral contraceptives (birth control pills) for a long time (5+ years)
•Having birth to many children (4+ yrs).
•Having many sexual partners

The importance of T-cell immunity!
A recent study has found that after one dose of Pfizer’s vaccine, patients had a strong T-cell response, and a boosted antibody response after their second dose. This is good news for many reasons so let’s read up.

This study found that people’s antibodies were moderately effective against the original strain after their first dose of Pfizer’s vaccine, less effective against the B.1.1.7 variant, and were unable to neutralize the B.1.351 variant. Read what I said, that’s only after ONE dose.

However, they had strong T-cell responses against ALL known variants after the first dose. So why is this important? To recap, T-cells stimulate B-cells to make antibodies. Antibodies are just your first line of defense which is what is initiated when you get a vaccine.

It’s our T-cells that are responsible for long-term immunity. When antibodies diminish after your initial inoculation, your T-cells-will tell your B-cells it’s time to produce more antibodies. As long as your T-cells still recognize this virus and inform your B-cells they need

to produce antibodies, the vaccine is still doing its job. Your antibodies will get built up over and over again. Just FYI your TCRs and your BCRs target different parts of the Coronavirus’ spike protein. Antibodies can prevent infection, your T cell-responses ensure that those

THIS IS A CATCHY & ATTENTION-GRABBING TITLE

Today, the Province will likely announce what zone of the reopening framework we'll shift into as of Tuesday. Whatever is announced, though, doesn't change what we need to do. Why?

Kindly read these 14 tweets & we'll tell you. (1/14)

Fun fact: COVID-19 does not follow us on Twitter. COVID doesn't know, or particularly care, if we're in the Red or Orange zone.

The behaviours of the virus do not depend on the current ‘zone’ of public health restrictions. And, to be perfectly candid, neither should ours. (2/14)

Once upon a time we used to say #WeAreInThisTogether in our messaging. Here's the thing: we are, but we aren't. Not everyone is experiencing this pandemic the same.

Nurses, doctors, paramedics & healthcare workers have experienced things much differently than most of us. (3/14)

Some people have been home & isolated for months, while others haven't been able to stop working.

Business owners & hospitality workers have been on a rollercoaster of openings & closures.

Students, parents, teachers & school staff have had to adapt to going from online (4/14)

to in-person learning (and back again) numerous times.
And this virus has disproportionately affected some groups more than others.

But there's the thing: while our experiences of this pandemic haven't been shared, we do have a shared goal.

And we can reach it together. (5/14)