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.

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Santiago
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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.
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Santiago
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.
ALL , DS , ADS
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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
What's machine learning?

Not a framework. Not a different language. Not the latest "cool" thing around the Internet.

Thread: Let me convince you that machine learning is the best thing since sliced bread 🍞.

Let's start with a simple exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function. It should be simple, right?

To avoid using a specific syntax, let's represent this function as follows:

y = f(X)

Where:
• X → The input number
• y → The result (0 if even, 1 otherwise)
• f → The function that does the work

We can now get to the interesting part.

If you are a programmer, writing this function is simple.

You know the relationship between X (the input number) and y (the output), so you can easily write the rules (the function.)

In machine learning, we don't write the rules. We learn them.

Imagine we had an algorithm that processes many samples (X → y), learns the relationship between them, and comes up with the correct rules.

We call these samples "training data," and they would like like this:

• 2 → 0
• 5 → 1
• 8 → 0
• 4 → 0
• 3 → 1
• ...
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Santiago
Santiago
@svpino
My recommendations to start with machine learning math:

• Statistics 110: Probability — Harvard University
• Essence of Linear Algebra — 3Blue1Brown
• Multivariate Calculus — Coursera

Links and 3 additional free resources here: https://t.co/fEmpmRKEvJ


If you are looking for more machine learning content, give me a follow and I’ll keep your feed interesting 😉.
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How did it catch on so quickly (just 3 years) to shift university curriculum, which is usually so slow/behind?

I wonder why Java was so respected and JS was not? Why/how was Java already seen as such a stable and mature language?

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I bring all this up because my impression of Java in 98 was that it had been around "forever" (as had C++ first developed in 79, but not standardized until 98!), but that's not. true at all. These were all new languages around the same time.

I'm sure the language designs had different merits, and the target applications meant a different perception and reception, but...

it sure seems like the industry (and academics) just decided Java and C++ were the stable mature ones and langs like JS were toys.

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God Hanuman played a prominent part in rescuing Mata Sitafrom the bondage of demon King of Lanka, Ravana.


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1. Hanuman Moola Mantra
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