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 👇

10 machine learning questions you should be able to answer.

I started experimenting with a questionnaire before interviewing candidates. Applicants submit a video or written answers to apply for the position.

Thread: Let's look at 10 of these questions.

The questionnaire solves a couple of problems:

1. Weeds out candidates that aren't ready for the job.

2. It significantly cuts down the time we spend talking synchronously.

One problem: It requires extra time from the candidate before the phone interview.

Here are some numbers on a job posting before using the questionnaire:

• 249 applicants
• 13 good applications

That's only 5%.

This sucks.

Numbers after using the questionnaire:

• 44 applicants
• 21 good applications

That's 47%.

This is much better.

I look at it this way:

Sooner or later, I'll ask these questions, and the candidate will have to answer them.

I'd rather give them space and the opportunity to think about their answers without any pressure.

Those who can't or don't want to answer won't apply. That's fine.

An introduction to one of the the most basic structures used in machine learning: a tensor.

🧵👇

Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

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Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

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The rank of a tensor refers to the tensor's number of axes.

Examples:

▫️ The rank of a matrix is 2 because it has two axes.
▫️ The rank of a vector is 1 because it has a single axis.

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The shape of a tensor describes the number of dimensions along each axis.

Example:

▫️ A square matrix may have (3, 3) dimensions.
▫️ A tensor of rank 3 may have (2, 5, 7) dimensions.

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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.

What's machine learning?

Not a framework. Not a different language. Not the latest "cool" thing around the Internet.

Thread: Let me convince you that machine learning is the best thing since sliced bread 🍞.

Let's start with a simple exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function. It should be simple, right?

To avoid using a specific syntax, let's represent this function as follows:

y = f(X)

Where:
• X → The input number
• y → The result (0 if even, 1 otherwise)
• f → The function that does the work

We can now get to the interesting part.

If you are a programmer, writing this function is simple.

You know the relationship between X (the input number) and y (the output), so you can easily write the rules (the function.)

In machine learning, we don't write the rules. We learn them.

Imagine we had an algorithm that processes many samples (X → y), learns the relationship between them, and comes up with the correct rules.

We call these samples "training data," and they would like like this:

• 2 → 0
• 5 → 1
• 8 → 0
• 4 → 0
• 3 → 1
• ...

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.

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

I am no psychiatrist or therapist, but I have been on the receiving end of verbal abuse, multiple times over decades, and managed to escape and recover from (most of) the trauma. I'm not sure that really "qualifies" me to say the following, in the academic sense, but...1/

I am seeing the press engaging in what I would call verbal abuse. When you invite a "guest" onto a program to present his or her POV, or to discuss their "expert" opinion on a subject, and then laugh at them, roll your eyes and talk over them, ask only rhetorical questions 2/

...and then accuse the guest of harming/doing disservice to the audience, you are behaving like an abuser. You are "informing" no one of anything other than your willingness to bully and abuse other human beings. Hosts with honest disagreements can and should ASK questions 3/

The hallmark of sincere disagreement is the listener who first checks for understanding of the speaker's POV, then asks why they say that. The host could challenge their arguments on the merits, with their evidence, then allow the guest to respond. This is how adults act. 4/

Immature, irrational, indoctrinated, and/or abusive, people talk OVER, mock, belittle, and accuse guests with different points of view of being "dangerous" or "harming" viewers/listeners. This is the behavior of deeply insecure people who know their ideas lack merit. 5/

I do want to talk a bit about connections, conflicts and information during a pandemic. Since @fordnation insinuated I have COI which I don't, let's take a look at a single instance where @fordnation and Hospital for Sick Children are both making interesting decisions.

Both share a powerful supporter in Peter Gilgan, who has been a great supporter of Sickkids

But his company, Mattamy Homes, has also substantially bankrolled Ontario Proud, which I have reason to believe pushed the Postmedia hit on my reputation.

Sickkids and Mattamy have very close ties. Here is Mary Federau, a longtime Sickkids exec who has moved to Mattamy but remains involved with the Sickkids foundation:

Mary's bio is too modest. It looks as though it has been scrubbed a bit over the last 72 hours. Here's the google cache from Jan 25: