11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

🧵👇

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.

👇

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.

Beginners guide to start with machine learning.

What you need:

1. A programming language
2. A place to write and run code
3. A way to deal with data
4. A way to visualize results
5. A cool algorithm
6. An introductory course
7. A project to solve

Here are my recommendations.

Most people recommend Python to start.

I do too.

You can do machine learning with many different languages, but today, Python is the best option.

My advice: Get comfortable writing code before looking into machine learning.

You don't have to be a great developer to start.

But if you are, it helps.

Everything you know about building software is helpful to build machine learning systems.

Good developers have the odds in their favor.

"How much Python do I need to know before starting?"

If you ask, you aren't ready yet.

Ideal scenario: You should be comfortable building software. Most people get here within a year.

Let's move on to the 2nd item from the list: A place to write and run your code.

Get familiar with Google Colab.

• It's free.
• Requires no setup to start.
• It's available from anywhere.

Alternative: Kaggle.

Most software founders, particularly in B2B, need to get radically better at sales. @Steli , who is the best person for explaining the scrappy stages in the beginning and then building out a team with scripts and processes, wrote a guide to:

For technical founders it is irrationally, obscenely hard to reverse years of programming (ba dum bum) that sales is a value-destroying activity. Sales is CLEARLY a value-creating activity, contingent on you have a value-creating product.

The world will not drop what they are doing to adopt your work. This is particularly true in B2B, where simply building a better mousetrap won't overcome the activation energy required to get people with additional non-mice problems to prioritize changing mousetraps today.

This is very non-obvious for founders because founders are not often people who *want* to be sold to. We often come from a background where trying out tools is a bit of a fun hobby. We like looking at all the options, making charts, and ripping out partially complete tests.

"This week I unsuccessfully trialed four software options for automating that thing that has been killing us. Our actual production process remains the same as last week. Don't worry; this was a great use of time." is not a thing you want to write in a progress report to manager.

🎉🎉 PHP 7.3 is here!

Here are the things I'm excited about.

🚂 Trailing commas in function/method calls!

This will certainly be a required styling choice for me. Same delightful benefits of trailing comma in multi-line PHP arrays, and JavaScript objects.

🧵 Less disgusting heredoc syntax!

Inlining heredoc strings in any way right now is grrrosssss. Now we get sensible capabilities. Everything that was wrong with it is now fixed!

(Ignore the bad syntax highlighting)

☠️ Finally, not-so-silent json_decode error detection!

This really sucked before, now it just sucks a bit less (who wants to pass a 4th param and pass 2 default params first? (helper function anybody?)

📜 Not horrible functions for getting the first and last item (or key) from an array!

Before you either strung a bunch of functions together or messed with internal array pointers. This is a much-needed improvement.

Incumbents almost always prevail with sustaining innovation

In disruptive circumstances, the entrants are likely to beat the incumbents

H/t Clayton Christensen, The Innovator’s Solution

⚡️🧠Impact = Environment x Skill

A framework for making big career decisions by @iambangaly (Reforge Partner, Former Instacart, Instagram)

Full post -> https://t.co/dU0l7e0BWR

🧵 Thread...

1/ Career decisions about your next role/opp are the most impactful decisions you might make in your career. As an operator, all of your eggs are in one basket at a time, and we get a limited number of swings at the plate. So getting good at these decisions is important...

2/ At FB, Bangaly managed Rotational PM's. At the end of a rotation, he'd have the same convo: How are you going to choose your next rotation/role?

As a result, Bangaly got a lot of reps guiding this type of decision and created a framework to help with it...

3/ The framework is Impact = Skills x Environment

The goal is not to try and boil this type of decision down to a spreadsheet of inputs that spits out the answer, but rather it helps with 4 things...

4/ It can help with:

- Identify individual variables that are inputs into the decision.
- Evaluate each individual variable in a structured way.
- Understand the relationship between each variable.
- Narrow the decision down to the most important variable

...