I passed first step ☺️ 100 citations. Always happy to collaborate and cooperate with teams. Hope to reach 1000 citations.
#paper
#article
#AI
#DataScience
#MachineLearning
#biometric
https://scholar.google.com/citations?user=_I3xMO8AAAAJ&hl=en

از بچه های هاروارد در مورد اهمیت ماشین لرنینگ و دیپ لرنینگ در میتینگ بزرگ ان ال پی خواهد گفت.
محمد نوشاد از دانشگاه هاروارد

https://www.linkedin.com/posts/activity-6973732091997929473-QH-L?utm_source=share&utm_medium=member_android

@OpenAI announced #Whisper, an automatic speech recognition model.

Blog post → https://openai.com/blog/whisper/
Research paper → https://cdn.openai.com/papers/whisper.pdf
Open-source code and models → https://github.com/openai/whisper

#ASR #Speech2Text

لینک گوگل کولب مربوط به پست whisper:

https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb

فارسی 🇮🇷: دوستان کسی اپ استارلینک رو نصب کرده در ایران؟ و آیا سرویس رایگان دارد؟ گفته می شود قراره از دوبی اینترنت رایگان داده شود.

تۆرکجه 🇮🇷: ایگیدلریمیزدن بیری واردیر استارلینک اپین ایراندا تلفونا یوکلیه؟ بونون پولوسوز سرویسی وارمی؟ دئیلیب دؽر پولوسوز دوبی دن اینترنت گله ایرانا.

https://play.google.com/store/apps/details?id=com.starlink.mobile


کانال
@iranian_in_poland

فارسی 🇮🇷: امروز روز ieee هست و اسپشیال آفر گذاشته چک کنید.

تۆرکجه 🇮🇷: بویون ieee گونی دی اؤزل آفر قویوبلار باخین بلکه دردینیزه دئیه.


https://ieeeday.org/special-offers/

State of AI Report 2022

Check it out here:
https://www.stateof.ai/

@ai_python

Dear Jalil,



Join us Tuesday for a panel discussion on Founding an AI Startup: Dos & Don'ts of Building a Company. Whether you're looking to build your first company or advance your current start-up this event is for you!


Speakers will cover:

Practical steps on how to get started creating your company.
Raising funds — When to start and what to expect.
Common pitfalls early-stage founders face and ways to overcome these obstacles.
Both technical and non-technical backgrounds are welcome to attend. Special thanks to AI Fund for co-hosting the event with us!

https://info.deeplearning.ai/e3t/Ctc/LX+113/cJhC404/VWjNDq33K-VXW7VFZsL58n2VWW4YmM-14R7MG0N4WpFgt3q3nJV1-WJV7CgZvTVBgwbC7jkx9CW287w0C2-CLKcW39ndPD4Nv1lkW8TC4W173h1MpW9dF33Z6BDbjZW35VSLY33z-2dW9j_GYQ7CJKdGVRCMVn6sKR0FW2_LtJK9hF3S_W4hpfCK5PCYVLW3yF78s6VZCSbW6fNz7h1wbQN7W24KfGX2Mdt1BW5TWP9j3DwdV5W8z_zKC6Xf_9nW1hw-W-415B4XW6SkfnY90xZ20W2xFNL688JsfDW8-0jTh3T7Pk_W990WmW7yKbJWW96R8JP4Y3RW7W8RjW8k5HQ5f1W4JqDfX5rRRCSW4Qxvnm4-Xyld3d8S1

کانال:
https://news.1rj.ru/str/Machinelearning_Kartal

There you go


(1/3) Until now, AI translation has focused mainly on written languages. Universal Speech Translator (UST) is the 1st AI-powered speech-to-speech translation system for a primarily oral language, translating Hokkien, one of many primarily spoken languages. bit.ly/3CJP3ew

(2/3) Hokkien, one of ~3k primarily spoken languages, has no standard writing system and very few human translators, making it even more difficult to create training data for our models and difficult to rely on Hokkien trannoscripts.
(3/3) By open-sourcing our benchmark data to the AI community and including our Hokkien system in a UST, we hope other researchers will build on this work. Someday all languages, written or unwritten, may no longer be an obstacle to mutual understanding. bit.ly/3VGxVif

مرکز آپای دانشگاه محقق اردبیلی در نظر دارد، تعداد محدودی از دانشجویان و فارغ التحصیلان ساعی و علاقمند به مباحث امنیت سایبری را در قالب کارآموزی و کار دانشجویی جذب نماید. افراد جذب شده بعد از طی مراحل آموزش و یادگیری، امکان همکاری در پروژه های این مرکز را خواهند داشت.
درصورتی که دارای برنامه و انگیزه کافی برای رشد هستید، شرح مختصری از وضعیت علمی و تحصیلی و اطلاعات تماس خود را به آدرس cert@uma.ac.ir ارسال نمایید.
(سوالات احتمالی را از طریق تلگرام طرح نمایید.)

سلام
من باشلامیشام neo4j اوخماقین و ایستیرم اورگندیکلریمی ده یوتوب دا قویام.

چالیشاجیام هفته ده بیر ویدیو قویام.

ایستسوز بئردا باخا بیلر سوز:

https://www.youtube.com/watch?v=XGJ6F8qDfcI&list=PLSxTo9u7OlJd3ExzsrJ9X7zhn0Bi0Tmzr

گوزل بیر حرکت ایگیدلریمیزدن ❤️❤️❤️ لایک کامنت یادان چیخماسین

تۆرکجه 🇮🇷: بو آپ آذربایجان اوچون دؽر. بولارین الینن توتاق.
فارسی 🇮🇷: یک اپلیکشن خوب برای آذربایجان و تمام ترکان ایران
Iran Azerbaycaninan turklara app:

Azerbaijan media adli bir güzel app:

https://play.google.com/store/apps/details?id=takce.resane


کانالیمیز:

https://news.1rj.ru/str/Ai_KartalOl

YES, WE DID IT.

ALLWAYS TEAM WORK WORKS.

PUBLISH A PAPER IN A JOURNAL WITH IF=7.3

❤️❤️❤️😍😍😍

Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

https://ieeexplore.ieee.org/document/9926136/authors#authors

Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.

کانال:
https://news.1rj.ru/str/Machinelearning_Kartal

تیم ورک همیشه جواب هست. 💪🏻💪🏻💪🏻💪🏻💪🏻💪🏻
ممنونم از همه اعضای تیممون.
مقاله در ژورنال با ایمپکت بالای هفت چاپ شد.
برای مسابقات دیگه کیا هستیممممممممممممممممممم؟؟؟؟؟؟؟؟؟؟؟؟؟ تبریککک به همه بچه ها.

@AndrewYNg well said:
If you have a best model which have the best results on test set. Celebrate it 🥳 that's wonderful but be careful it is not always best on your production. So, we always have gap between working model on testset and real world applications.
#DataScience

https://twitter.com/jalilnkh/status/1585740724087259140?t=zr4zKR68MCRgGlAschy8cg&s=19

وبینار با ارزش وْ خوب
ثبت نام کنید و شرکت کنید

https://www.linkedin.com/posts/vitomir-%C5%A1truc-17b41a14_biometrics-ieeebiometricscouncil-responsibleai-activity-6991827649631281152-k0xZ?utm_source=share&utm_medium=member_android


کانالیمیز:

https://news.1rj.ru/str/Machinelearning_Kartal

سلام خدمت تمام همراه عزیز آذربایجان مئدیا پیامرسان تورک چت آپدیت شد
یکی از بهترین ویژگی ها هم امنیت بالا و نیاز نبودن فیلترشکن
ساخت گروه و .....
جهت استفاده کاملا رایگان کافیست در آذربایجان مئدیا تورک چت را انتخاب کنید🙏

حمایت و اشتراک گذاری خواهشاً 🙏🙏🙏
@azerbaycan_media

You don't need to buy a GPU for machine learning work!

There are other alternatives. Here are some:

1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks

Spend your time focusing on your problem.

#منابع

✳️ @AI_Python