Deep learning channel

سلام دوستان، امیدوارم خوب باشید در زمینه تویین‌های دیجیتال و تبلیغات چند ایده جذاب دارم که بدم نمیاد رووش کار جدی‌تری بکنم. اگه از دوستان هوش مصنوعی کار صاحب تجربه کسی که علاقه‌مند باشه این ایده رو در قالب استارت‌اپ شروعش کنیم بهم پیام بده. @ikj1992 دنبال فردی میگردم که در زمینه پردازش زبان طبیعی حداقل سه سال فعالیت داشته باشه و روحیه کنجکاوی بالا داشته باشه. روی یه محصول، تجربه کار داشته باشه حتما. راستی تهران باشه که احتمالا جلسات حضوری هم خواهیم داشت ولی کارا بصورت ریموت میره جلو و در میزان و نوع زمان گذاشتن هم دستمون بازه در شروع همکاری... قاعدتا هم در قالب همبنیان‌گذار باهم همکاری خواهیم داشت.... توجه: لطفا پیامتون همراه با رزومه شما باشه وگرنه بخاطر مزاحمت‌های پیش‌امده در گذشته باعرض پوزش پیامتون پاسخ داده نمیشه. تشکر، ایمان #همکاری #استارتاپ #هوش_مصنوعی

✅ رونمایی از مدل های زبانی فارسی جدید ۱. مدل زبانی فارسی سیلک (Sialk) توسعه‌یافته از پایه (from scratch) با استفاده از دادگان اختصاصی فارسی ۲. مدل زبانی بزرگ فارسی آهوران (Ahoran) مدل زبانی بزرگ فارسی با یادگیری پیوسته (continual pretraining) و دادگان جدید و به‌روز برای افزایش دقت و کارایی ۳. مدل زبانی بزرگ فارسی آوا (Ava) طراحی شده (finetune) برای وظایف خاص و بهره‌برداری بهینه از مدل‌های پیشین ⏰ زمان: دوشنبه ۱۱ تیر | ساعت ۹:۳۰ 🏢 مکان: محل دائمی نمایشگاه‌های بین‌المللی تهران | سالن خلیج فارس لینک پخش زنده مراسم: https://lnkd.in/dvq2rMtv @irandeeplearning @bigdataworkgroup

دوره جامع یادگیری عمیق فرصت ویژه برای علاقه مندان به هوش مصنوعی و دیپ‎لرنینگ 40 ساعت محتوا + 10 ساعت کلاس آنلاین تعاملی + منابع مطالعاتی ✅دسترسی به محتوا + کلاسهای رفع اشکال + تمرین و پروژه و تصحیح + مدرک + معرفی به بازارکار 🎉کد تخفیف30 درصدی ویژه عید فطر:

fitr403

اعتبار تا 24 فروردین 🧑‍🏫مدرس: علیرضا اخوان‌پور توضیحات جلسات آنلاین: https://class.vision/blog/deeplearning1403-hybrid/ 🎞ویدوی ضبط شده از جلسه آنلاین پرسش و پاسخ و توضیحات این دوره: مشاهده 🎁در انتهای دوره دانشجویان برتر به شرکت های فعال در زمینه هوش مصنوعی برای انجام مصاحبه معرفی خواهند شد. 🗓⏱ساعت و روز برگزاری: دوشنبه ها ساعت 18 الی 19 (از ۲۷ فرودین ۱۴۰۳) لینک ثبت نام و توضیحات محتوای 40 ساعته: مشاهده

وبینار رایگان هوش مصنوعی و یادگیری عمیق (ظرفیت محدود) 🔗لینک ثبت نام | یکشنبه 20 اسفند - ساعت 18 - تفاوت AI، ML و Deep Learning - کاربردهای هوش مصنوعی - حوزه های کاری - راهنمای ورود به دنیای هوش مصنوعی 🎓ارائه دهنده: علیرضا اخوان پور مدرس دانشگاه و مدیر فنی مجموعه دانش بنیان شناسا

We will be commencing in the next 30 minutes. If you are interested, please feel free to join us.

If you're interested in federated learning, particularly in medical imaging, we invite you to join our seminar tomorrow (Friday) at 11:00 a.m. Iran time! Zoom: https://oist.zoom.us/j/95908496615?pwd=akxZNmprLzNXY212TFh0ZWQ1ZlNyUT09 Meeting ID: 959 0849 6615 Passcode: 767685 Speaker: Prof. Shadi Albarqouni, Computational Medical Imaging Research, University of Bonn Title: Unlocking the Potential of Federated Learning in Medical Imaging Abstract: Deep Learning (DL) stands at the forefront of artificial intelligence, revolutionizing computer science with its prowess in various tasks, especially in computer vision and medical applications. Yet, its success hinges on vast data resources, a challenge exacerbated in healthcare by privacy concerns. Enter Federated Learning, a groundbreaking technology poised to transform how DL models are trained without compromising data security. By allowing local hospitals to share only trained parameters with a centralized DL model, Federated Learning fosters collaboration while preserving privacy. However, hurdles persist, including heterogeneity, domain shift, data scarcity, and multi-modal complexities inherent in medical imaging. In this illuminating talk, we delve into the clinical workflow and confront the common challenges facing AI in Medicine. Our focus then shifts to Federated Learning, exploring its promise, pitfalls, and potential solutions. Drawing from recent breakthroughs, including a compelling MR Brain imaging case study published in Nature Machine Intelligence, we navigate the landscape of secure and efficient AI adoption in healthcare. Bio: Shadi Albarqouni, a pioneering figure in Computational Medical Imaging, serves as a Professor at the University of Bonn and an AI Young Investigator Group Leader at Helmholtz AI. With significant roles at Imperial College London, ETH Zurich, and the Technical University of Munich (TUM), Shadi's impact reverberates through his 100+ publications in esteemed journals and conferences. His expertise extends beyond academia, with contributions as an Associate Editor at IEEE Transactions on Medical Imaging and evaluator for national and international grants like DFG, BMBF, and EC. Recognized with awards like the DAAD PRIME Fellowship, Shadi fosters collaboration through AGYA and ELLIS memberships and initiatives like the Palestine Young Academy and the RISE-MICCAI community, focusing on innovative medical solutions and knowledge transfer to emerging countries. Explore more about his work at https://albarqouni.github.io/.

If you're interested in federated learning, particularly in medical imaging, we invite you to join our seminar tomorrow (Friday) at 11:00 a.m. Iran time! Zoom: https://oist.zoom.us/j/95908496615?pwd=akxZNmprLzNXY212TFh0ZWQ1ZlNyUT09 Meeting ID: 959 0849 6615 Passcode: 767685 Speaker: Prof. Shadi Albarqouni, Computational Medical Imaging Research, University of Bonn Title: Unlocking the Potential of Federated Learning in Medical Imaging Abstract: Deep Learning (DL) stands at the forefront of artificial intelligence, revolutionizing computer science with its prowess in various tasks, especially in computer vision and medical applications. Yet, its success hinges on vast data resources, a challenge exacerbated in healthcare by privacy concerns. Enter Federated Learning, a groundbreaking technology poised to transform how DL models are trained without compromising data security. By allowing local hospitals to share only trained parameters with a centralized DL model, Federated Learning fosters collaboration while preserving privacy. However, hurdles persist, including heterogeneity, domain shift, data scarcity, and multi-modal complexities inherent in medical imaging. In this illuminating talk, we delve into the clinical workflow and confront the common challenges facing AI in Medicine. Our focus then shifts to Federated Learning, exploring its promise, pitfalls, and potential solutions. Drawing from recent breakthroughs, including a compelling MR Brain imaging case study published in Nature Machine Intelligence, we navigate the landscape of secure and efficient AI adoption in healthcare. Bio: Shadi Albarqouni, a pioneering figure in Computational Medical Imaging, serves as a Professor at the University of Bonn and an AI Young Investigator Group Leader at Helmholtz AI. With significant roles at Imperial College London, ETH Zurich, and the Technical University of Munich (TUM), Shadi's impact reverberates through his 100+ publications in esteemed journals and conferences. His expertise extends beyond academia, with contributions as an Associate Editor at IEEE Transactions on Medical Imaging and evaluator for national and international grants like DFG, BMBF, and EC. Recognized with awards like the DAAD PRIME Fellowship, Shadi fosters collaboration through AGYA and ELLIS memberships and initiatives like the Palestine Young Academy and the RISE-MICCAI community, focusing on innovative medical solutions and knowledge transfer to emerging countries. Explore more about his work at https://albarqouni.github.io/.

Graph Convolutional Networks: Unleashing the power of Deep Learning for Graph data 🗓زمان برگزاری (به صورت آنلاین): شنبه 28 بهمن ماه 1402 ⏱ساعت 17:30 الی 19 📍آدرس اتاق مجازی: https://vc.sharif.edu/ch/cognitive @irandeeplearning | @cvision

This talk will start very soon, please join if you are interested