Ace the SQL Interview by Nick Singh

Practice the most common SQL & Data Interview Questions and Learn SQL.

Navigational hashtags: #armknowledgesharing #armsites
General hashtags: #sql

Write faster Python code, and ship your code faster

Faster and more memory efficient data
- Articles: Learn how to speed up your code and reduce memory usage.
- Products: Observability and profiling tools to help you identify bottlenecks in your code.

Docker packaging for Python- Articles: Learn how to package your Python application for production.
- Products: Educational books and pre-written software templates.

Navigational hashtags: #armknowledgesharing #armsites
General hashtags: #python #development #docker

NeetCode: A better way to prepare for coding interviews

The best free resources for Coding Interviews. Period.
- Organized study plans and roadmaps (Blind 75, Neetcode 150).
- Detailed video explanations.
- Public Discord community with over 30,000 members.
- Sign in to save your progress.

Links:
- Roadmap
- Practice (Core Skills, Blind 75, Neetcode 150, Neetcode All)
- Algorithms and Data Structures for Beginners (course) paid
- Advanced Algorithms (course) paid

Navigational hashtags: #armknowledgesharing #armsites #armtutorials
General hashtags: #leetcode #python #algorithms #datastructures #interviewpreparation #technicalinterview

Leetcode for ML
Super neat set of machine learning coding challenges.

It could be useful to prep for an exam or ML interview.

Link

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General hashtags: #ml #dl #machinelearning #deeplearning

Oh Shit, Git!?!

Git is hard: screwing up is easy, and figuring out how to fix your mistakes is fucking impossible. Git documentation has this chicken and egg problem where you can't search for how to get yourself out of a mess, unless you already know the name of the thing you need to know about in order to fix your problem.

- I did something terribly wrong, please tell me git has a magic time machine!?!
- I committed and immediately realized I need to make one small change!
- I need to change the message on my last commit!
- I accidentally committed something to master that should have been on a brand new branch!
- I accidentally committed to the wrong branch!
- I tried to run a diff but nothing happened?!
- I need to undo a commit from like 5 commits ago!
- I need to undo my changes to a file!
- I give up

Link

Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #git #versioncontrol #github #gitlab

Immersive linear algebra by J. Ström, K. Åström, and T. Akenine-Möller

"A picture says more than a thousand words" is a common expression, and for text books, it is often the case that a figure or an illustration can replace a large number of words as well. However, they believe that an interactive illustration can say even more, and that is why they have decided to build their linear algebra book around such illustrations. They believe that these figures make it easier and faster to digest and to learn linear algebra (which would be the case for many other mathematical books as well, for that matter). In addition, they have added some more features (e.g., popup windows for common linear algebra terms) to their book, and they believe that those features will make it easier and faster to read and understand as well.

After using linear algebra for 20 years times three persons, they were ready to write a linear algebra book that they think will make it substantially easier to learn and to teach linear algebra. In addition, the technology of mobile devices and web browsers have improved beyond a certain threshold, so that this book could be put together in a very novel and innovative way (they think). The idea is to start each chapter with an intuitive concrete example that practically shows how the math works using interactive illustrations. After that, the more formal math is introduced, and the concepts are generalized and sometimes made more abstract. They believe it is easier to understand the entire topic of linear algebra with a simple and concrete example cemented into the reader's mind in the beginning of each chapter.

Link: Book

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General hashtags: #math #linearalgebra #algebra

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Bash Scripting Tutorial for Beginners by Herbert Lindemans

Learn bash scripting in this crash course for beginners. Understanding how to use bash scripting will enhance your productivity by automating tasks, streamlining processes, and making your workflow more efficient.

⌨️ (00:00) Introduction
⌨️ (03:24) Basic commands
⌨️ (06:21) Writing your first bash script
⌨️ (11:29) Variables
⌨️ (14:55) Positional arguments
⌨️ (16:23) Output/Input redirection
⌨️ (23:23) Test operators
⌨️ (25:19) If/Elif/Else
⌨️ (28:37) Case statements
⌨️ (32:16) Arrays
⌨️ (34:12) For loop
⌨️ (36:03) Functions
⌨️ (41:31) Exit codes
⌨️ (42:30) AWK
⌨️ (45:11) SED

Link: Video

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General hashtags: #bash #cmd #terminal

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DevOps for Data Science by Alex K Gold

In this book, you’ll learn about DevOps conventions, tools, and practices that can be useful to you as a data scientist. You’ll also learn how to work better with the IT/Admin team at your organization, and even how to do a little server administration of your own if you’re pressed into service.

Link: Direct

Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #devops #mlops #datascience

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Exceptional Resources for Data Science Interview Preparation. Part 3: Specialized Machine Learning

In the previous article, I shared materials for preparing for the stage on Classical Machine Learning.

In this article, we will look at materials that can be used to prepare for the section on specialized machine learning.

Table of contents
- Resources - Deep Learning
- Natural Language Processing
- Computer Vision
- Graph Neural Networks
- Reinforcement Learning
- Recommender Systems
- Time Series
- Big Data
- Let’s sum it up
- What’s next?

NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I published it on medium.com. So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.

Links:
- Medium (eng)
- Habr (rus)

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General hashtags: #interview #interviewpreparation #machinelearning #ml #deeplearning #dl #nlp #cv #rl #gnn #recsys

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Mathematics Of Machine Learning by MIT

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.

Link: Direct

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General hashtags: #math #maths #mathematics #ml

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