Kitob o'qimay qo'yganizga qancha vaqt bo'ldi yoki So'ngi o'qib tugatgan kitobingiz ? 🤔

p.s) Badiiy kitob o'qimay qo'yganimga ham ancha bo'libdi, so'ngi o'qigan (o'qiyotgan) kitoblarim, maqolalarim faqat texnalogik bo'layotgan ekan 😕

Bularning bazilari:
- Using Asyncio in Python - 2020 (✅️️️️️️️)
- Full-Stack Web Development with Go (☑️)
- Cracking the coding interview (☑️)
- System Design Interview (2020) (☑️)
- Parallel loops (✅)
- ...

Ba'zida hamkasblarim orasida ham o'z fikrlarimni tushuntirish, (yetkazib) berish qiyin bo'layotganini his qilaman, natijada nimanidur (texnalogik) tushuntirish kerak bo'lsa uni doskada chizib, gapirib tushuntirmasam o'zimni ko'nglim to'lmaydigan holat bo'lmoqda. 🤷‍♂️

Dasturchilik sohada muloqot kamligi bunga sabab bo'lishi mumkin (shu uchun dasturchilarni o'z dunyosi bor deb uylashsa kerak 🪐), shu bois dasturchilarga ko'proq networking'da bo'lish tavsiya qilinadi (aslida bu hamma sohada zarur).

Xullas biroz yerik chekinish (chalg'ish) qilish maqsadida baddiy yoki chet nashiryoti mansub qanday kitob tavsiya qila olasiz 👇✍️

Books channel

Python Async with HTTP

p.s) bu maqolada ham mavzu yaxshi yoritilgan ekan. Python kodini tezlashtirish og'riqli tajriba bo'lishi mumkin lekin udalab bulmaydigan task emas :)

Bugungi maqolalar:

* https://guicommits.com/how-python-async-works/

* https://guicommits.com/effective-python-async-like-a-pro/

* https://tamerlan.dev/multithreaded-programming-in-python/

* https://www.kdnuggets.com/2021/06/make-python-code-run-incredibly-fast.html

* https://youtu.be/x1ndXuw7S0s


#daily_articles

2021 yildan buyon 1084 (per_page 20 item) vakansiyaga resume topshirgan ekanman (hh.uz): 🤷‍♂️

60+ Invitations 👍
360+ Rejections 👎

(Telegram kanalardagi e'lonlarga apply qilganlarimni aytmasa ham bo'ladi🤦‍♂️😅)

p.s) Har bir interview olingan rejection bu aslida fail emas. Sizda interview topshirishda qaysi savolarga tayor turish va qaysi texnalogiyalarni o'rganib borish kerak ekanligni haqida tajriba rivojlanib boradi va har bir interview'da iloji boricha feedback so'rang aynan qaysi jihatingiz maqul bo'lmagani yoki ularni ikkilantirayotgani haqida.

More: https://azimjon.com/blog/100ta-intervyu-nima-orgatdi/


Siz nechta vakansiyalarga topshirgansiz?! (uyalmasdan yozavering😉) 👇📝

List of interesting topics to revise this weekend:

YouTube:

- Build a Video Streaming Service like YouTube, Netflix using AWS S3, CloudFront and React

- Configure a Docker Nginx Reverse Proxy Image and Container

- vWeather part3(Vue3+ element-plus + echart + rapidapi ) (not finished yet)

Medium:

- Web API performance: profiling Django REST framework

- Things you should know before running celery in Production

- Tackling Unexplained High RAM and CPU Usage in Django + Redis + Celery Server

- Handling a Billion JSON Data Points in Django: Our Validation Solution

Devops-journey:

- Jenkins bilan Docker CI/CD

GitHub:

- Data Structures and Algorithms (MAANG star and forks 😉)

Linkedin:

- grpc in GO

- Asynchronous python boost performance and efficiency for real world apps (not finished yet)

Books:

- Introducing Go - 2016 (in progress)

⚡ 21 Must-Have Cheat Sheets for Data Science Interviews: Unlocking Your Path to Success

▪SQL

1. SQL Basics Cheat Sheet
2. The Essential SQL Commands Cheat Sheet for Beginners
3. SQL Cheat Sheet – Technical Concepts for the Job Interview

▪Python

4. Python Cheat Sheet
5. Python Cheat Sheet
6. Comprehensive Python Cheatsheet

▪R

7. RStudio Cheatsheets

▪Data Structures

8. Data Structures Reference
9. An Executable Data Structures Cheat Sheet for Interviews

▪Data Manipulation

10. Pandas Cheat Sheet for Data Science
11. Pandas Cheat Sheet
12. Data Wrangling With pandas Cheat Sheet

▪Data Visualization

13. Data Visualization Cheat Sheet
14. Data Visualization Cheat Sheet
15. Data Visualization Cheat Sheets

▪Statistics & Probability

16. A Comprehensive Statistics Cheat Sheet for Data Science Interviews
17. The Most Comprehensive Stats Cheat Sheet
18. Statistics Cheat Sheet

▪Algorithms & Models

19. Top Prediction Algorithms
20. Your Ultimate Data Science Statistics & Mathematics Cheat Sheet
21. Cheat Sheet for Machine Learning Models

©️DS com

@ladybytes

Haqiqatda ham bu ancha qo'l kelgan. Nafaqat dasturlashda boshqa o'zim qiziqtirgan ma'lumotlarni internetda oson va tez izlab topib o'rganish uchun ham.

Dasturlashni o'rganmochiman 1-chi qaysi tili o'rganay deb surayotganlarning hammasiga tavsiya:

- Ingliz\Rus tilini o'rganish bu eng yaxshi yo'l .....

.
.
.
Balki foydasi tegar: https://news.1rj.ru/str/DrBekOlimjon/704
.

Kubernetes or Docker Swarm ?


Pick Kubernetes for your app when you have:
1. Complex microservices to manage.
2. High availability and fault tolerance is high priority.
3. Working with a diverse infrastructure (hybrid and multi-cloud deployments)

Select docker swarm when you have:

→ Simple containerized applications.
→ Smaller-scale deployments.
→ Cost-effective solution.

Both of them are cool tech. Choose 1 based on your need. )

More: https://semaphoreci.medium.com/docker-swarm-vs-kubernetes-9cc38812a4d8

Bonus: https://levelup.gitconnected.com/how-to-deploy-your-applications-using-docker-swarm-6917f7a9b899

Regression va Classification

1.2a-rasmdagi model input xususiyatlariga (kvadrat metr va yotoq xonalari) asoslanib uyning narxini predict qiladi.
Bu regressiya - chunki model uzluksiz raqamni qaytaradi (kategoriyalash o'rniga).

1.2b dagi model molekulaning kimyoviy tuzilishini input sifatida oladi va erish/qaynash nuqtalarini ham predict qiladi.
Bu ko'p o'zgaruvchan regressiya (multivariate) - chunki u bir nechta raqamni ko'rsatadi.

1.2c-rasmdagi model input sifatida restoran haiqda feedbackni oladi va sharhning ijobiy yoki salbiy ekanligini taxmin qiladi.
Bu binary classification - chunki model kirishni ikkita toifadan biriga belgilashga harakat qiladi. Output vektor input ning har bir toifaga tegishli bo'lish ehtimolini o'z ichiga oladi.

1.2d va 1.2e ko'p sinfli tasniflash (multiclass classification) - muammolarini tasvirlaydi. Bu yerda model inputni N > 2 toifadan biriga kiritadi. Birinchi holda, input audio fayl va model qaysi musiqa janrini o'z ichiga olganligini taxmin qiladi. Ikkinchi holda, input tasvir va model qaysi obyektni o'z ichiga olishini taxmin qiladi. Har bir holatda, N o'lchamdagi vektor N toifadagi ehtimolliklarni o'z ichiga oladi.

©️ @ladybytes