Deep learning channel

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

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/.

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/.

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

If you're interested in self-supervised learning, join our meeting today at 9:30 a.m. Iran time! Zoom: https://oist.zoom.us/j/92257619030?pwd=d2IvVktjQUVPME8rdFhqWFlmNERRQT09 Meeting ID: 922 5761 9030 Passcode: 595720 Speaker: Dr. Yuki M. Asano, Assistant Professor, QUVA Lab, University of Amsterdam Title: Self-Supervised Learning from Images and Videos using Optimal Transport Abstract: In this talk we will learn more about self-supervised learning -- the principles, the methods and how properly utilizing video data will unlock unprecendented visual performances. I will first provide a brief overview of self-supervised learning and show how clustering can be combined with representation learning using optimal transport ([1] @ ICLR'20 spotlight). Next, I will show how this method can be generalised to multiple modalities ([2] @NeurIPS'20) and for unsupervised segmentation in images ([3] @CVPR'22) and in videos ([4] @ICCV'23). Finally, I show how optimal transport can be utilized to learn models from scratch from just a single Walking Tour video that outperform those trained on ImageNet, demonstrating high potential for future video-based embodied learning ([5] @ICLR'24).  [1] Self-labelling via simultaneous clustering and representation learning Asano, Rupprecht, Vedaldi.  ICLR, 2020 [2] Labelling unlabelled videos from scratch with multi-modal self-supervision. Asano, Patrick, Rupprecht, Vedaldi. NeurIPS 2020 [3] Self-supervised learning of object parts for semantic segmentation. Ziegler and Asano. CVPR 2022 [4] Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations. Salehi, Gavves, Snoek, Asano. [5] Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video. Venkataramanan, Rizve, Carreira, Avrithis, Asano. ICLR 2024.  

✅ چهارمین دوره‌ «رویداد هوش مصنوعی امیرکبیر AAISS» شامل دو بخش سخنرانی علمی و کارگاه های آموزشی 👤دعوت از محققین مراکز دانشگاهی بنام داخلی و خارجی EPFL، Alberta، ETH، Monash، Illinois, OIST, UCI, Waterloo, Ottawa, Caltech, Hong Kong, McGill, Western, UCSD, USC, AUT, TMU, IUST و شرکت های بزرگ نظیر Google، Microsoft، eBay، Netflix, Huawei, Snapp ⚡️مناسب برای تمامی علاقه‌مندان هوش‌مصنوعی ⏱ زمان برگزاری از ۱۵ لغایت ۲۵ آذرماه 🌐 ثبت نام و کسب اطلاعات بیشتر: https://aaiss.ir 🕑 برنامه زمانبندی ارائه ها: https://aaiss.ir/schedule 🆔 کانال اطلاع رسانی رویداد: @aaiss_aut 💠کد تخفیف ویژه:‌ IranDeepLearning @irandeeplearning @ceit_ssc

🎥 آموزش شبکه های عصبی گرافی https://class.vision/product/graph-neural-network/ سرفصلهای دوره | اسلایدها | ویدیوی معرفی | کدها | فصل اول به عنوان نمونه ویدیو | کاربرد شبکه های عصبی گرافی این آموزش شامل مباحث تئوری+ عملی بوده و 13 کد در فریم ورک تنسرفلو و پایتورچ جئومتریک مورد بحث قرار گرفته است. کد تخفیف 20 درصدی ویژه اعضای کانال: irandeeplearning