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.)

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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.

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

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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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A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

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Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

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"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

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.

Google's interview system is not great but there are various reasons why it feels less than it is. Most candidates don't know what the goal of our interviews are. First rule, it is not about finding an optimal solution or any solution at all.

You are interviewed for multiple skills simultaneously. Cognitive skills, communication, leadership are a few to name. If the point is not finding a solution, then what is it? Let me explain.

Your interviewers try to understand what it feels like to work with you on a daily basis. An interview question is just a tool in achieving that, it is not there to specifically measure your skills on a topic but a tool to understand the depth of your thinking.

Before the interview starts, ask them what they want to get out of this interview. Good interviewers should already have a plan and a set of expectations. Ask them what you should do. Don't start coding yet. Ask them you should produce. Discussion, diagrams, pseudo code, code?

Then, start cracking the question. List whatever questions you think it is important to solve this question, ask your edge cases. Get to a point where you are discussing about pros/cons of the solutions. These steps are critical. Don't just start coding. Have a consensus first.

Setting up Git/GitHub to work with R/RStudio can be challenging.

Here's a thread to walk you through the process step by step.

This thread also lives in blog post form. #rstats

Preamble: the best overall resource, and one I rely on extensively, is Happy Git with R by @JennyBryan and @jimhester_.

If you have additional questions about Git/GitHub + R/RStudio, this should be your first

The first step to set things up is to install Git.

Chapter 6 of Happy Git with R lays out the process for Mac, Windows, and Linux users.

I’m on a Mac so Git came pre-installed on my computer. I was able to verify that I had Git installed using the terminal in RStudio.

The next step is to configure Git.

This is covered in Chapter 7 of Happy Git with R , though I show what I believe to be a slightly easier process using the edit_git_config() function from the {usethis} package.

When you complain about an open source project's management you'll get the reply, "Use something else if you don't like it!" That'd be true except for the tiny problem that *many* projects also strive for complete monopolistic dominance.

Let me tell you about Java in early 2k:

I was programming in Java since it's release, and all through the late 90s into the early 2000s. At first Java was very determined to unseat C++ and it used various propaganda tactics to turn C++ and malloc into a weird enemy for all of us to hate. This worked great!

Everything was fine though because there were plenty of jobs in tech because of the dot-com boom, and then suddenly there were not jobs because of the dot-com bomb.

Overnight it seemed as if the *only* way to get a job was at giant companies, and they *loved* Enterprise Java.

Now, this is the golden ticket for many open source projects and corporations. They all want to be the top dog #1 in the industry for their particular use, and once they gain that position they become abusive by ignoring user needs and extracting wealth from them.

With Java the dominance was so insane that *every* language had to copy Java's weirdness to survive. If you wonder why a language has some odd CamelCase things it's because early Python, Ruby, and others had to copy Java's semantics and style to even be considered "serious".