Workshopimiz 15:00’da boshlanadi!

Barchani kutib qolamiz.
Sizlarni ajoyib sovg’alar ham kutadi(Bu hozircha sir😉)

Bizda hammasi tayyor!

Alloh manfaatli qilsin!

Videoga olib keyin kanalga joylaymiz)

Bugungi Workshopdagi materiallar

GitHub: https://github.com/YoshlikMedia/GDG-WorkShop

Colab: https://bit.ly/cartpole

Keynote: https://bit.ly/3a3UHNI

On May 29, a grandiose event was held by the GDG Tashkent, in which various representatives of the IT industry of Uzbekistan shared their knowledge with the audience. Thanks to this event, the ML Club was able to contribute to the development of IT in Uzbekistan, and in particular the AI sector.

Our club was represented by two ambitious and experienced speakers from this field:

Bekhruz Nutfilloev (AI/ML developer at Uzinfocom)-gave a lecture about Reinforcement Learning, where it is used, and what the advantages of this ML algorithm over others.

Mahmood Sodikov (ML/DS team leader in Huawei)-share with the participants about Deep Learning and showed how it is applied using examples of his projects and shared his experience in studying DL.

We are sure their knowledge was very useful to the participants of the meeting and would like to take the opportunity to thank the speakers and participants.

📌 Muhim habarlar!

Yozda mazza qilib code yozishga tayyormisiz🏖🍹
Ushbu imkoniyatlar aynan siz uchun!

1. Yozda Birga Kod Yozamiz ☀️ 2022

📆 Deadline: 15-iyun
▶️ Boshlanadi: 1-iyul
🕐 Davomiyligi: 12 hafta

🔗 Topshiring: bit.ly/yoz-kod

👨‍🏫 Mentorlar:
- Khamidulla Inoyatov @ Booking
- Azimjon Pulatov @ Meta

2. FAANG Interview

✅ Talablar: Python, English, good communication & problem solving skills, love for books📚❤️

🕰 Time: 5am-7am (no days off)
📍 Location: Online, Meeting on Saturdays (if possible)
📅 Duration: 3-4months
📚 Study Materials: Leetcode questions, books related to coding and interviews. Starting with "Cracking the Coding Interview"

Contact: @sabokhat_k
About me: https://sabohat.me

⚠️ Only for girls

🔗 Please share it with your friends &
Join @Study_with_a_buddy channel

Coming soon...

Data Science sohasida ilmiy faoliyat olib borish bilan kompaniyada ishlashning farqlari qanday?

Bu galgi suhbatimiz, 18 iyun soat 18.00 telegram voice chatida bo'lib o'tadi.

Suhbatdoshimiz, Firuz Juraev Koreyaning Sungkyunkwan Universitetida Data Science sohasida ilmiy izlanish doktoranti va MyTaxi kompaniyasida Data Scientist.

Suhbatda:
-Data bozori
-Xalqaro Universitetda o'qishning qulayliklari
-Data Science job interview lari qanday bo'lishi haqida bilib olasiz!

p.s: Savollaringizni izohlar bo'limida qoldirishingiz mumkin!

#problem

Listdagi n ta eng katta va eng kichik elementlarni chiqaring.

Input:
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
3

Output:
[42, 37, 23]
[-4, 1, 2]

Eng optimal yechimni commentda kutaman)

#solution

Yuqoridagi misolga ko’pchilik turli xil yechimlar keltirgan.

Quyida 3xil yechim bo’yicha natijalarni ko’rishingiz mumkin.

Avvali kichik massiv uchun oladigan bo'lsak, kichik massivlarda sortlash judayam tezkor bo'lgani hisobiga yuqoridagi input uchun sortlash yaxshi yechimlardan hisobladi. Lekin massiv kattalashishni boshlasa, yechimni o'zgartirish kerak bo'ladi. Katta massivlar uchun heapdan foydalangan optimalroq hisoblanadi.


P/s: Albatta bu fikr judayam nisbiy olib qaralgan comentda o’z taqqoslashlaringiz natijalarini yuboring. #discuss

⛈🎧💻…

Bugundan yaxshi AI Enginner ruknidagi postlarni boshlaymiz.

15 ta kichik postlarda AI Enginner bilishi kerak bo’lgan narsalar haqida qisqacha yozaman. Qo’llab turasiz degan umiddaman😉

Virtual Environment(Conda)

Bu mavzuda juda ko’p marta gaplashganmiz. Data Science, Machine Learning sohalarida eng kerakli narsalardan biri hisoblanadi.

Anaconda men eng sevadigan python environment. Bunda pythonning istalgan versiyasi bilan o’z muhitingizni qursangiz bo’ladi va har bir muhitda kerakli versiyalarni o’rnata olasiz va o’chira olasiz. Shuningdek buni Miniconda, Miniforge, Mamba kabi turlari ham bor. Shaxsan o’zim Miniconda va Miniforge ishlataman. Chunki bular ARM64da ham bemalol ishlaydi. Anaconda esa faqat x86_64 uchungina ishlaydi.

conda create -n <yourenvname> python=3.9
pip install -r requirements.txt
conda activate <yourenvname>
conda deactivate

conda env list
conda env remove -n <yourenvname>

quyida terminalda environment yaratish va o’chirish qanchalik oddiyligini ko’rsangiz bo’ladi


#AIEngineer #part_1

Virtual Environment

(VENV)

venv ham juda zo’r hisoblanadi. Chunki siz o’rnatmoqchi bo’lgan package siz ishlayotgan papkada bo’ladi. Lekin yomon tomoni unda python version control yo’q.

venvda muhit yaratish.

cd <projectfolder>
python -m venv <name>
source <name>/bin/activate
pip install -r requirements.txt
deactivate


(Jupyter Notebook)

yana bir muhit tizimi desak ham bo’ladi. lekin bu muhit uchun ham yuqoridagi venv yoki conda kerak. Bu ipythonda oson ishlash uchun va ikernel ixlosmandlari uchun hisoblanadi.

jupyterda muhit yaratish:

pip install ipykernel
python -m ipykernel install --user --name=<env_name>



P/s: Aslida python muhitlari juda ko’p lekin bular eng ko’p data science va ML Engineerlari tomonidan ishlatiladiganlari.

Mana bu link’da yanada ko’proq bu haqida gaplashilgan.

#AIEngineer #part_2

2-o’rin🎉

If you don’t understand, don’t worry about it

😅