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Workshopimiz 15:00’da boshlanadi!
Barchani kutib qolamiz.
Sizlarni ajoyib sovg’alar ham kutadi(Bu hozircha sir😉)
Barchani kutib qolamiz.
Sizlarni ajoyib sovg’alar ham kutadi(Bu hozircha sir😉)
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Bugungi Workshopdagi materiallar
GitHub: https://github.com/YoshlikMedia/GDG-WorkShop
Colab: https://bit.ly/cartpole
Keynote: https://bit.ly/3a3UHNI
GitHub: https://github.com/YoshlikMedia/GDG-WorkShop
Colab: https://bit.ly/cartpole
Keynote: https://bit.ly/3a3UHNI
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Barchadan uzr so’rab qolardim auditoriyadan videoga olishni so’ragandim. Lekin unutganlar shekilli. Faqat short videolar olishibdi xolos. Ularni iloji boricha yuklashga harakat qilaman.
Qatnashganlar o’z feedbacklarini qoldirsalar yaxshi bo’lardi.
Qatnashganlar o’z feedbacklarini qoldirsalar yaxshi bo’lardi.
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Forwarded from ML Community Uzbekistan (Shakhriyor Kh)
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.
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.
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📌 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
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
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Image-Text Pre-training with Contrastive Captioners
Birvaqtning o’zida Image Encoder va Unimodal Text Decoder.
Source: https://ai.googleblog.com/2022/05/image-text-pre-training-with.html
CoCa -> Contrastive + CaptioningBirvaqtning o’zida Image Encoder va Unimodal Text Decoder.
Source: https://ai.googleblog.com/2022/05/image-text-pre-training-with.html
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Forwarded from maroon bells🪐
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!
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!
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DeCoder
#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
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
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