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 👇

I told everyone that I didn't care.

"Screw math! I've never been great with it, so I'm not starting with machine learning to fail at the end."

That was many years ago.

Math is still hard, but I don't think you should be scared at all. Here is why: 🧵👇

[1] One thing changed my mind: school pushed me to the deep end of the pool, and I was forced to swim.

I had to face my fears, and I started machine learning and realized that the math involved is not as scary (or as much) as some people believe.

👇

[2] Probably one of the most frequent questions I get is around the math needed for machine learning.

Answer:

▫️ Probabilities and Statistics
▫️ Linear Algebra
▫️ Calculus

But it turns out that this is not helpful.

👇

[3] What are you gonna do with this?

Are you going to learn all there's is about linear algebra before looking into machine learning?

How about probabilities and statistics? Or calculus? Do you know how extensive calculus is?

👇

[4] Many people stop looking into machine learning as soon as somebody tells them that calculus is a pre-requisite.

First of all, the amount of calculus you truly need is minuscule.

Second, without proper direction, there's no way you'll find your way through the noise.

👇

I learned enough AI/ML in 12 months to change my life forever.

You can do the same, and it all starts with a killer process to learn new things.

You can steal my approach. Every detail on the latest issue of my newsletter: https://t.co/9gaenLByFb.

Make it work for you.

Interviews aren't only about technical skills.

Here are some questions to help you prepare.

🧵👇

Explain what you have been working on for the past few weeks.

What are the most exciting parts about that work?

What portion of it do you consider boring and why?

[2 / 10]

What specific libraries and frameworks are you familiar with?

What's the minimum set of libraries and frameworks that you'd recommend to any practitioner?

[3 / 10]

What kind of problems have you worked on in the past?

Can you list the specific use cases related to each one of these?

[4 / 10]

What is the most exciting project you have ever worked on?

What was your role and responsibilities on that project?

Why do you think your work on that project was important?

[5 / 10]

I sat next to one of the smartest, most successful software engineers in the country for over 10 years.

Here's what I learned.

Fast is 10 times better than good.

9 out of 10 times, a "good enough" solution is all you need to unlock time, money, and attention.

Focus relentlessly on being first. Then take the time to be right.

Fast, then right.

Ship more frequently.

The faster you show your work to your users, the faster you'll hear their feedback, you'll adapt, and you'll ship again.

A question you should ask every day:

What can I do to ship this one day before planned?

Technical debt is a good thing.

Everyone hates technical debt. I think they don't know how to take advantage of it.

When used appropriately, technical debt means working on what truly matters and deferring anything that can wait.

No debt ⟶ You waited too long.

Ask, don't think.

People burn their time every day instead of asking for help.

You don't get extra points for working harder, especially when a little help will make you faster.

Time is your most valuable resource. Don't waste it.

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.

I'm not pro-murdering babies.
I'm pro-Becky who found out at her 20 week anatomy scan that the infant she had been so excited to bring into this world had developed without life sustaining organs.

Can any MP ask the government why early treatment kits to stop viral replication & infection are not made available cheaply & prescription free in pharmacies as they are in India? Is it lives we want to save or is it Big Pharma profits? Because the two are mutually exclusive.

There could even be competing kits, & the decision which to take could be left to doctors & patients, as happens with most medicines. The problem is that there is not one of these kits available cheaply & prescription free anywhere in the West.

Doxycycline, Ivermectin, Azithromycin, Hydroxychloroquine, Zinc, Vitamin D, Vitamin C, omega-3s, all approved as safe drugs decades ago. This is clearly not an issue of safety. It should be left to a doctor's experience & judgement whether they are effective or not, not academia.

A GP could simply state on his website: of all the kits I recommend Kit Z, then Kit Y etc. No need to wait hours for consultations, as soon as the symptoms are clear the self administered treatment begins, and thousands of lives are saved, as well as hospitals remain underwhelmed

There is no need to go to ER or ICU if people can buy early treatment kits from pharmacies. There is no need for any hospital to be overwhelmed. This problem could be resolved in days. There is simply no political will to save lives as it means to go against Big Pharma profits.

#Thread on Tantra

The word ’Tantra’ literally means ‘loom’ in Sanskrit. There are several differing explanations as to what this word means. According to one source, this name is supposed to be a pun, and in order to understand it, one has to first understand the word ‘sutra’.

The key texts of Hinduism are known as sutras, a word which also means ‘formula or thread’. Therefore, if a sutra is a single thread of thought, then Tantra would be the loom that produces these threads of thoughts into a whole system of thoughts.

As a comparison, another source suggests that Tantra is a combination of two words, ‘tattva’ and ‘mantra’, which mean the science of cosmic principles and the science of mystic sound and vibration respectively.

In other words, Tantra may be regarded to be “the application of cosmic sciences with a view to attain spiritual ascendancy.”

In the beginning of Satyuga, Lord Shiva appeared in the form of Swacchandanatha, with his five mukha(mouth), namely Ishana, Tatpurusha, Sadyojata,

Vamadeva & Aghor. These mukhas represent his 5 energies, namely, Consciousness, Bliss, Will, Knowledge & Action. He manifested Tantras through his 5 mukhas (energies), some given by each of his mouth while others recited by him through two or more than two mouths simultaneously.

I have created a step-by-step guide to building a #CorsiRosenthalBox. Thank you @JimRosenthal4 & @CorsIAQ for approving it. Displaying it in waiting rooms, offices, schools, and libraries will help more people stay safe this season. Please DM me if you need a PDF or another size.

Also, thank you to @LadyScorcher and @amandalhu!