BC TECH
Saved by @SteveeRogerr

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.)

🧵👇

 1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.
2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.
3. Write a function that finds the duplicate number in an unsorted array containing every number from 1 to 100.

Only one of the numbers will appear twice.
4. Write a function that removes every duplicate value in an array.

There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.
5. Write a function that finds the largest and smallest number in an unsorted array.
6. Write a function that finds a subarray whose sum is equal to a given value.
7. Write a function that finds the contiguous subarray of a given size with the largest sum.
8. Write a function that, given two arrays, finds the longest common subarray present in both of them.
9. Write a function that, given two arrays, finds the length of the shortest array that contains both input arrays as subarrays.
10. Write a function that, given an array, determines if you can partition it in two separate subarrays such that the sum of elements in both subarrays is the same.
11. Write a function that, given an array, divides it into two subarrays, such as the absolute difference between their sums is minimum.
I'd love to see some answers!

If you want more content on software engineering, machine learning, and adjacent topics, give me a follow. I post threads like this every week. You can enjoy more of this content here: @svpino.

More from Santiago

Santiago
Santiago
@svpino
12 machine learning YouTube videos.

On libraries, algorithms, tools, and theory.

↓

1. Jupyter Notebooks: https://t.co/HqE9yt8TkB

2. Pandas: https://t.co/aOLh0dcGF5

3. Matplotlib: https://t.co/tKADpmihkh

4. Seaborn: https://t.co/s8EUxh6x1f


5. Numpy: https://t.co/pJoc0Lfjwm

6. Decision Trees: https://t.co/tKtUpO1K3l

7. Neural Networks: https://t.co/bc7emyjc9q

8. Scikit-Learn: https://t.co/LrKG7cMxRq


9. TensorFlow: https://t.co/fhO6T9sblU

10. PyTorch: https://t.co/5w9mJxijdd

11. Essense of Linear Algebra: https://t.co/o3kOnxl90i

12. Essense of Calculus: https://t.co/rfo7v0cpR4
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Santiago
Santiago
@svpino
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.

👇
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Santiago
Santiago
@svpino
11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇

😜

Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

😜

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

👇

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

👇

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

👇
MACHINE LEARNING
Santiago
Santiago
@svpino
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.
DS
Santiago
Santiago
@svpino
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.
DS

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foone
foone
@Foone
Keybort

This one is a MicroWeb Touch-1 XT/AT. Kinda.


See if you look up that keyboard you find the Deskthority page on it, showing this.
It's clearly pretty similar, but there are some differences.


Also that one uses a PC/XT/AT DIN5 connector, and this one uses some kind of RJ connector.


This one isn't in the greatest of shape, but considering I got it from the Chuck Colby estate sale, it's surprising it isn't completely water-damaged.


It's got some minor rust damage inside, where the metal clips rubbed off on the PCB.
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Simon DeDeo
Simon DeDeo
@SimonDeDeo
So, today, for the first time in 25 (!) years of Apple, I downgraded. From the 2016 MacBook Pro to my 2013, which I had kept in a drawer...


It was pretty simple to do—Apple Time Machine backups let me do it with one click.

That first tweet captures, in two pictures, how badly Apple has “lost the plot” (to quote @wylieprof). On the right is the Apple MagSafe adapter, from 2013. On the left, what I had “upgraded” to.

Thanks, Apple! I really was nostalgic for worrying about yanking my computer off the table.

Oh and I really appreciated not knowing if my computer was charging. What was great was the little whoop sound you used, so that the speaker before me could be informed I was charging my laptop.
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Mrkhtake2
Mrkhtake2
@mrkhtake2
Today we’re going to talk statistics and semantics. I’m going to do so via the medium of one of Twitter’s favourite stats 1.7% (aka “intersex is as common as red heads.”). The statistic comes from Fausto-Sterling’s work, which you can see a review of

To understand the statistic, we have to drill down into what the meaning of the word intersex is. Most people think of hermaphrodites. Let’s stress at the beginning there has been no example ever of a human being with both sets of working reproductive organs.

There is a very tiny percentage of people who are born with both testicular and ovarian tissue (what Fausto-Sterling calls “true hermaphrodites”). And when I say tiny, we’re talking intersex tiny, as in 0.0117/1,000 live births, or one in 1,000,000. These are very complex cases.

So, how do we arrive at 1.7%? What Fausto-Sterling does is apply a wider scope. Instead, she argues that intersex is anything that doesn’t fit into a platonic ideal. The platonic ideal, in this case, being a phenotypically perfect XX female and a phenotypically perfect XY male.

This means that intersex, used in the context of the 1.7% statistic, includes people for whom there is no sexual ambiguity. Take for example Klinefelter syndrome (XXY males). As Fausto-Sterling acknowledges, many males can go their whole life and not realise they are XXY.
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Shannon Woodward
Shannon Woodward
@shannonwoodward
Ok, I’ve told this story a few times, but maybe never here. Here we go. 🧵👇


I was about 6. I was in the car with my mother. We were driving a few hours from home to go to Orlando. My parents were letting me audition for a tv show. It would end up being my first job. I was very excited. But, in the meantime we drove and listened to Rush’s show.

There was some sort of trivia question they posed to the audience. I don’t remember what the riddle was, but I remember I knew the answer right away. It was phrased in this way that was somehow just simpler to see from a kid’s perspective. The answer was CAROUSEL. I was elated.

My mother was THRILLED. She insisted that we call Into the show using her “for emergencies only” giant cell phone. It was this phone:


I called in. The phone rang for a while, but someone answered. It was an impatient-sounding dude. The screener. I said I had the trivia answer. He wasn’t charmed, I could hear him rolling his eyes. He asked me what it was. I told him. “Please hold.”
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Brian Balfour
Brian Balfour
@bbalfour
⚡️🧠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

...
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Share for the benefit of everyone.

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1/ Something strange is happening with people’s sex lives. 20-somethings are having sex later and less frequently than previous generations. I spent several months digging into this for @theatlantic. https://t.co/5ehzmWY9wi


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KrishnaLeela
KrishnaLeela
@Talesofkrishna
🌿𝑻𝒉𝒆 𝒔𝒕𝒐𝒓𝒚 𝒐𝒇 𝒂 𝑺𝒕𝒂𝒓 : 𝑫𝒉𝒓𝒖𝒗𝒂 & 𝑽𝒊𝒔𝒉𝒏𝒖

Once upon a time there was a Raja named Uttānapāda born of Svayambhuva Manu,1st man on earth.He had 2 beautiful wives - Suniti & Suruchi & two sons were born of them Dhruva & Uttama respectively.
#talesofkrishna https://t.co/E85MTPkF9W


Now Suniti was the daughter of a tribal chief while Suruchi was the daughter of a rich king. Hence Suruchi was always favored the most by Raja while Suniti was ignored. But while Suniti was gentle & kind hearted by nature Suruchi was venomous inside.
#KrishnaLeela


The story is of a time when ideally the eldest son of the king becomes the heir to the throne. Hence the sinhasan of the Raja belonged to Dhruva.This is why Suruchi who was the 2nd wife nourished poison in her heart for Dhruva as she knew her son will never get the throne.


One day when Dhruva was just 5 years old he went on to sit on his father's lap. Suruchi, the jealous queen, got enraged and shoved him away from Raja as she never wanted Raja to shower Dhruva with his fatherly affection.


Dhruva protested questioning his step mother "why can't i sit on my own father's lap?" A furious Suruchi berated him saying "only God can allow him that privilege. Go ask him"
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Bharadwaj
Bharadwaj
@BharadwajSpeaks
Today is Jayanti of Ahilyabai Holkar

She was a widow who became Malwa's greatest queen. Defended our country & personally led armies into battle.Never plundered anyone. Developed Malwa into a prosperous kingdom. Rebuilt temples destroyed by Mughals. Built Dharmashalas at Tirthas


Unfortunately, her memory has been blemished by inaccurate representations in modern popular media.

The serial "Punyashlok Ahilyabai" depicts her as ultra secularist feminazi icon who questions Hindu rituals & Pandits while patronising Sufis.

This is far from historical truth!


Ahilyabai was born on May 31 1725 to Patil Mankoji Shinde.

She was from Dhangar clan(classified as OBC today). While Dhangars were traditionally into herding, they took up arms & held several military /civil roles in various eras of history(especially during Maratha resurgence)


In 1737, when she was 12, Ahilyabai Holkar was married to Khanderao , son of Subedar Malhar Rao Holkar.

By the time she was married, fortunes of Holkar family had already waxed.

Malhar Rao was a Subedar of territory comprising 30 Parganas yielding a revenue of at least 7 Lakh


In 1741, when Ahilyabai was newly married, her father -in-law Malhar Rao Built Indore palace & ordered the kanungo to populate the settlement with a promise of protection to traders.

This began rise of Indore from its background as modest trading town Indrapuri to a great city
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