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Santiago
@svpino 5 years, 8 months ago 945 views

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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
@svpino

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.

Santiago
@svpino

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.

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Santiago
@svpino

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.

Santiago
@svpino

20 machine learning questions that will make you think.

(Cool questions. Not the regular, introductory stuff that you find everywhere.)

🧵👇

1. Why is it important to introduce non-linearities in a neural network?

2. What are the differences between a multi-class classification problem and a multi-label classification problem?

3. Why does the use of Dropout work as a regularizer?

👇

4. Why you shouldn't use a softmax output activation function in a multi-label classification problem when using a one-hot-encoded target?

5. Does the use of Dropout in your model slow down or speed up the training process? Why?

👇

6. In a Linear or Logistic Regression problem, do all Gradient Descent algorithms lead to the same model, provided you let them run long enough?

7. Explain the difference between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent.

👇

8. What are the advantages of Convolution Neural Networks (CNN) over a fully connected network for image classification?

9. What are the advantages of Recurrent Neural Networks (RNN) over a fully connected network when working with text data?

👇

Santiago
@svpino

I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."

This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.

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it's done.

a visual enigma machine simulator: https://t.co/eLE4Sb97HW

Biz Stone
@biz

Originally, tweet length was 160 minus username length. One day, I turned to Jack and pointed out that I got one more character than him. He said, "That's not fair. We should standardize." I agreed. He said, "How about 140?" I said, "Sure." And he updated the code.

Last year, we upgraded tweet length to 280 and guess what? Use of 'please' is up 54% and 'thank you' is up 22%. I love that! Also, people are asking more questions and having more conversations. All this, and the majority of tweets are still under 140. It worked.

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Michael Dearing
@mcgd

An irony of facebook's situation.

Sheryl got her MBA at Harvard. One of the most famous cases (Extra Strength Tylenol) in one of the most famous classes (Business History) she took: in 1982, someone put cyanide in Extra Strength Tylenol capsules and killed 7 people in Chicago.

What do you do when someone turns your product into a weapon? When they use the system you built to harm? James Burke, CEO of J&J, was shockingly open with the public, he pulled the product and made significant packaging changes to make product safer (but not tamper-proof).

He over-shared every step along the way re investigation, redesign, stood up as both CEO and human. The reintroduction of new Extra Strength Tylenol succeeded. Burke saved the brand.

But four years later it happened again. A killer put cyanide in the capsules, this time a woman in Yonkers died. Same CEO, Burke, pulled the product again, completely changed the form factor from capsule to caplet and relaunched *again*. It worked *again*. How'd they do that?

Burke (CEO) tapped J&Js goodwill bank account w/ the public. Two big withdrawals from that bank account in four years + 8 dead bodies! But his honesty, openness, humanity (choked up about the deaths more than once), humility kept the goodwill bank balance positive the whole time.

Shannon Woodward
@shannonwoodward

Ok, I’ve told this story a few times, but maybe never here. Here we go. 🧵👇

Wish I had the audio of Rush Limbaugh telling me off on the phone on his show when I was six. In the meantime, RIP.
— Shannon Woodward (@shannonwoodward) February 17, 2021

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.”

Santiago
@svpino

Here is a simple example of a machine learning model.

I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.

It's TensorFlow and Keras.

If you are starting out, this may be a good puzzle to solve.

The goal of this model is to learn to multiply one-digit

It is a good example of coding, what is the model?
— Freddy Rojas Cama (@freddyrojascama) February 1, 2021

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Ameda Ksetram is located in the banks of Vembanad Lake. Temple is very unique and Powerful Sarpa Kavu.
Parashurama did Prathishta here. Vilakku which was lighted by Parasurama is still seen inside the temple.

The oil from this lamp is given to bhakthas for recovering from illness.

Sthala puranam says that, Once, when Parashurama was travelling through Kerala, he reached this place. As it was evening, he stepped into the Lake for Sandhya Vandanam
At that Sapthamathrukkal 👇

(Brahmini, maheshwari, kowmari, vaishnavi, varahi, indirani and chamundi) were travelling there on a tortoise through the water. The tortoise was unable to move, because the Water had made its way for Parashurama. The place where the tortoise became Still came to be known as 👇

Aama Ni Nidayam' ( Aama means tortoise) and later become ‘Ameda’. Parashurama came to know about the journey of Sapthamathrukkal. Thus, he did Prathishta for the Sapthamathrukkal here.
7 forms of Aadi Parashakthi are Sapthamathrukkal. Veerabhadra and Ganesha have Sannidhi here.

The main Garbhagriham in the temple is the Sapthamathrukkal. There are Sannidhi to Nagas, Nagayakshi and Vishnu too. Sarparadhana or snake worship is done here. Naga Panchami is famous here. Those who have Naga Dosham come here and get relieved of their Doshas.
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$The Astro Sapien\u2728$

The Astro Sapien✨
@TheAstroSapienn

Here is my Thread on 'Ketu (South Node) Through Nakshatras' The 27 Constellations 🙏❣.

Do check your “Past life Mastery” which KETU Represents🤞🏻.

1. Ketu in Ashwini Nakshatra:

Here native is Great at Financial Planning, Face reading, understanding other’s mind and intention, Driving Fast they don’t like but they are wayyy smooth over driving Fast. This is also great position which can make native Great Surgeon,

Neurologist, Psychologist.
These natives are Selfless and Sacrifice self for others. They gets Fame and Success but they just don’t care.

2. Ketu in Bharani Nakshatra:

They are good at Flirting, Attracting opposite sex, studing ancient subjects. They are also Great at Creativity and Artistic activities. The are lazy but they are really open minded who handles situations really well.

In Past life they have handles many things which is now helping them to make things easier in this life. They are great Leaders too.

They are really good at Hard work and Smart work both. And yes just as Ashwini they don’t much care about Fame and Success!

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Erik Torenberg
@eriktorenberg

1/ Here’s a list of conversational frameworks I’ve picked up that have been helpful.

Please add your own.

2/ The Magic Question: "What would need to be true for you

1/\u201cWhat would need to be true for you to\u2026.X\u201d

Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?

A thread, co-written by @deanmbrody: https://t.co/Yo6jHbSit9
— Erik Torenberg (@eriktorenberg) December 4, 2018

3/ On evaluating where someone’s head is at regarding a topic they are being wishy-washy about or delaying.

“Gun to the head—what would you decide now?”

“Fast forward 6 months after your sabbatical--how would you decide: what criteria is most important to you?”

4/ Other Q’s re: decisions:

“Putting aside a list of pros/cons, what’s the *one* reason you’re doing this?” “Why is that the most important reason?”

“What’s end-game here?”

“What does success look like in a world where you pick that path?”

5/ When listening, after empathizing, and wanting to help them make their own decisions without imposing your world view:

“What would the best version of yourself do”?

Noah Smith
@Noahpinion

Remember: Just because an idea is old doesn't mean it's good.

Knowing whether old ideas are more likely to be good requires understanding whether conditions have changed in important ways.

Horse archery was an amazing method of warfare for over a thousand years...then people invented guns, and suddenly this great idea that had stood the test of time became obsolete.

Then again, when underlying conditions don't change much, tried-and-true approaches are probably better.

"Learning the lessons of history" usually just means assuming ergodicity and stationarity in an informal time-series model...

(end)

David Rothkopf
@djrothkopf

If there is one thing I have learned it is that people love bullshit. They buy bullshit stories from professional bullshit artists, accept bullshit philosophies, purchase bullshit how-to-books, vote for bullshitting politicians.

At first, I resisted believing this because it is illogical and dumb and frankly, doesn't reflect well on the human race of which I am a member (and thus have long been hopeful that it is more of a distinction than it actually turns out to be.)

But if you actually are interested in facts you begin to notice how little most people care for them. Sometimes it is because they are heard to learn and bullshit is so much more widely and easily available. Sometimes it is because facts make them uncomfortable about themselves.

Sometimes it is because just one tiny fact will upset a whole worldview that has grown as comfortable as an overstuffed chair. (Settling into such worldviews is a lot like settling into chairs like that because the longer you're there and the deeper you sink...

...the harder it is to hoist yourself up and out of your fat-assed intellectual laziness.) But I actually think the reason people have such an appetite for bullshit goes deeper, goes to an aspect of ourselves we just can't accept.