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
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
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
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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Santiago
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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Santiago
Santiago
@svpino
Free machine learning education.

Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!

Here are 6 of these programs. Pick one and get started!

↓


Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany

Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.

https://t.co/Uxx97WPCfR


Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani

This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.

https://t.co/cKzpDOBVl1


Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny

A theoretical course focusing on design principles and best practices to design deep neural networks.

https://t.co/1TFUAIrAKb


Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov

A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.

https://t.co/hD5no8Pdfa
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JBD
@rakyll
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.
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it's done.

a visual enigma machine simulator: https://t.co/eLE4Sb97HW
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Robin Berjon
Robin Berjon
@robinberjon
Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
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@matiwinnetou
(1) Some haters of #Cardano are not only bag holders but also imperative developers.

If you are an imperative programmers you know that Plutus is not the most intuitive -> (https://t.co/m3fzq7rJYb)

It is, however, intuitive for people with IT financial background, e.g. banks

(2)

IELE + k framework will be a real game changer because there will be DSLs (Domain Specific Languages) in any programming language supported by K framework. The only issue is that we need to wait for all this

(3) Good news is that the moment we get IELE integrated into Cardano, we get some popular langs. To my knowledge we should get from day one: Solidity and Rust, maybe others as well?

List of langs: https://t.co/0uj1eBfrYj, some commits from many years ago..

@rv_inc ?

#Cardano

(a) Last but not least, marketing to people with Haskell, functional programming with experience and decision makers in banks is a tricky one, how do you market but not tell them you want to replace them. In the end one strategy is to pitch new markets, e.g. developing world

(b) As banks realize what is happening they maybe more inclined to join - not because they would like to but because they will have to - in such cases some development talent maybe re-routed to Plutus / Cardano / Algorand / Tezos
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YouTube ads at ~$35B run-rate vs Netflix at ~$31B run-rate
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https://t.co/CYtqYorhzH
Archived: https://t.co/ncz5ruwE2W


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