🎯 Deep Learning and Neural Networks Symposium and Workshop
👨🏻🎓 Speaker introduction:
Dr. Mohammad Ganjtabesh,
University of Tehran
Title: Bio-inspired Learning of Visual Features in Shallow and Deep Spiking Neural Networks
Abstract: To date, various computational models have been proposed to mimic the hierarchical processing of the ventral visual pathway in the cortex, with limited success. In this talk, we show how the association of both biologically inspired network architecture and learning rule significantly improves the models' performance in challenging invariant object recognition problems. In all experiments, we used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. We start with a shallow network, in which neurons in the higher trainable layer are equipped with STDP learning rule and they progressively become selective to intermediate complexity visual features appropriate for object recognition. Then, a deeper model comprising several convolutional (trainable with STDP) and pooling layers will be presented, in which, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Finally, we show how reinforcement learning can be used efficiently to train a deep SNN to perform object recognition in natural images without using any external classifier and the superiority of reward-modulated STDP (R-STDP) over the STDP in extracting discriminative visual features will be discussed.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
👨🏻🎓 Speaker introduction:
Dr. Mohammad Ganjtabesh,
University of Tehran
Title: Bio-inspired Learning of Visual Features in Shallow and Deep Spiking Neural Networks
Abstract: To date, various computational models have been proposed to mimic the hierarchical processing of the ventral visual pathway in the cortex, with limited success. In this talk, we show how the association of both biologically inspired network architecture and learning rule significantly improves the models' performance in challenging invariant object recognition problems. In all experiments, we used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. We start with a shallow network, in which neurons in the higher trainable layer are equipped with STDP learning rule and they progressively become selective to intermediate complexity visual features appropriate for object recognition. Then, a deeper model comprising several convolutional (trainable with STDP) and pooling layers will be presented, in which, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Finally, we show how reinforcement learning can be used efficiently to train a deep SNN to perform object recognition in natural images without using any external classifier and the superiority of reward-modulated STDP (R-STDP) over the STDP in extracting discriminative visual features will be discussed.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
Telegram
attach 📎
❌ ظرفیت کارگاه یادگیری عمیق و شبکه های عصبی به اتمام رسید.
⚠️ علاقمندان به شرکت در این کارگاه که متأسفانه تا کنون موفق به ثبت نام نشده‌اند می توانند به تلگرام @loop_academy پیام ارسال کنند تا در لیست انتظار احتمالی قرار گیرند.
☎️ 021 - 2591 7391
🌐 www.loopacademy.ir
⚠️ علاقمندان به شرکت در این کارگاه که متأسفانه تا کنون موفق به ثبت نام نشده‌اند می توانند به تلگرام @loop_academy پیام ارسال کنند تا در لیست انتظار احتمالی قرار گیرند.
☎️ 021 - 2591 7391
🌐 www.loopacademy.ir
🎯 Deep Learning and Neural Networks Symposium and Workshop
👨🏻🎓 Speaker introduction:
Dr. Milad Mozafari,
CerCo, CNRS, France
Title: Reconstructing Natural Scenes from fMRI Patterns using Bi-directional Generative Neural Networks
Abstract: Decoding and reconstructing images from brain imaging data is a research area of high interest. Recent progress in deep generative neural networks has introduced new opportunities to tackle this problem. Here, we employ a recently proposed large-scale bi-directional generative adversarial network, called BigBiGAN, to decode and reconstruct natural scenes from fMRI patterns. BigBiGAN converts images into a 120-dimensional latent space which encodes class and attribute information together, and can also reconstruct images based on their latent vectors. We trained a linear mapping between fMRI data, acquired over images from 150 different categories of ImageNet, and their corresponding BigBiGAN latent vectors. Then, we applied this mapping to the fMRI activity patterns obtained from 50 new test images from 50 unseen categories in order to retrieve their latent vectors, and reconstruct the corresponding images. Pairwise image decoding from the predicted latent vectors was highly accurate (84%). Moreover, qualitative and quantitative assessments revealed that the resulting image reconstructions were visually plausible, successfully captured many attributes of the original images, and had high perceptual similarity with the original content. This method establishes a new state-of-the-art for fMRI-based natural image reconstruction, and can be flexibly updated to take into account any future improvements in generative models of natural scene images.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
👨🏻🎓 Speaker introduction:
Dr. Milad Mozafari,
CerCo, CNRS, France
Title: Reconstructing Natural Scenes from fMRI Patterns using Bi-directional Generative Neural Networks
Abstract: Decoding and reconstructing images from brain imaging data is a research area of high interest. Recent progress in deep generative neural networks has introduced new opportunities to tackle this problem. Here, we employ a recently proposed large-scale bi-directional generative adversarial network, called BigBiGAN, to decode and reconstruct natural scenes from fMRI patterns. BigBiGAN converts images into a 120-dimensional latent space which encodes class and attribute information together, and can also reconstruct images based on their latent vectors. We trained a linear mapping between fMRI data, acquired over images from 150 different categories of ImageNet, and their corresponding BigBiGAN latent vectors. Then, we applied this mapping to the fMRI activity patterns obtained from 50 new test images from 50 unseen categories in order to retrieve their latent vectors, and reconstruct the corresponding images. Pairwise image decoding from the predicted latent vectors was highly accurate (84%). Moreover, qualitative and quantitative assessments revealed that the resulting image reconstructions were visually plausible, successfully captured many attributes of the original images, and had high perceptual similarity with the original content. This method establishes a new state-of-the-art for fMRI-based natural image reconstruction, and can be flexibly updated to take into account any future improvements in generative models of natural scene images.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
Telegram
attach 📎
Loop Academy | آکادمیِ لوپ
📣 برنامه زمانی کارگاه یادگیری عمیق و شبکه های عصبی (Deep Learning and Neural Networks) ⚙️ برگزار کنندگان: پژوهشکده علوم شناختی و مغز دانشگاه شهید بهشتی و آکادمی لوپ. 📅 تاریخ برگزاری کارگاه: 3 الی 5 اسفند ماه 98. ⭕️ برای مشاهده جزئیات بیشتر و ثبت نام روی…
📣 خواهشمند است شرکت کنندگان کارگاه یادگیری عمیق و شبکه های عصبی در روزهای برگزاری کارگاه لپ تاپ به همراه داشته باشند و بسته نرم افزاری Anaconda و همینطور پکیج Tensorflow 2 را برای بخش های Hands-On نصب داشته باشند.
📢 برای کسب اطلاعات بیشتر به کانال تلگرامی مراجعه فرمایید:
@LoopAcademy
🌐 www.loopacademy.ir
📢 برای کسب اطلاعات بیشتر به کانال تلگرامی مراجعه فرمایید:
@LoopAcademy
🌐 www.loopacademy.ir
🎯 Deep Learning and Neural Networks Symposium and Workshop
👨🏻🎓 Speaker introduction:
Dr. Mohammad Rostami,
University of Pennsylvania
Title: Learning to Transfer Knowledge Through Embedding Spaces
Abstract: The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and avoid learning redundant information. Inspired from learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. In this talk, we address the challenges of knowledge transfer through embedding spaces that capture and store hierarchical knowledge.
In the first part of the talk, we focus on the problem of cross-domain knowledge transfer. We first address zero-shot image classification, where the goal is to identify images from unseen classes using semantic descriptions of these classes. We train two coupled dictionaries which align visual and semantic domains via an intermediate embedding space. We then extend this idea by training deep networks that match data distributions of two visual domains in a shared cross-domain embedding space. Our approach addresses both semi-supervised and unsupervised domain adaptation setting.
In the second part of the talk, we investigate the problem of cross-task knowledge transfer. Here, the goal is to identify relations and similarities of multiple machine learning tasks to improve performance across the tasks. We first address the problem of zero-shot learning in a lifelong machine learning setting, where the goal is to learn tasks with no data using high-level task descriptions. Our idea is to relate high-level task descriptors to the optimal task parameters through an embedding space. We then develop a method to overcome the problem of catastrophic forgetting within continual learning setting of deep neural networks by enforcing the tasks to share the same distribution in the embedding space. We further demonstrate that our model can address the challenges of domain adaptation in the continual learning setting.
We demonstrate that despite major differences, problems within the above learning scenarios can be tackled through learning an intermediate embedding space.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
👨🏻🎓 Speaker introduction:
Dr. Mohammad Rostami,
University of Pennsylvania
Title: Learning to Transfer Knowledge Through Embedding Spaces
Abstract: The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and avoid learning redundant information. Inspired from learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. In this talk, we address the challenges of knowledge transfer through embedding spaces that capture and store hierarchical knowledge.
In the first part of the talk, we focus on the problem of cross-domain knowledge transfer. We first address zero-shot image classification, where the goal is to identify images from unseen classes using semantic descriptions of these classes. We train two coupled dictionaries which align visual and semantic domains via an intermediate embedding space. We then extend this idea by training deep networks that match data distributions of two visual domains in a shared cross-domain embedding space. Our approach addresses both semi-supervised and unsupervised domain adaptation setting.
In the second part of the talk, we investigate the problem of cross-task knowledge transfer. Here, the goal is to identify relations and similarities of multiple machine learning tasks to improve performance across the tasks. We first address the problem of zero-shot learning in a lifelong machine learning setting, where the goal is to learn tasks with no data using high-level task descriptions. Our idea is to relate high-level task descriptors to the optimal task parameters through an embedding space. We then develop a method to overcome the problem of catastrophic forgetting within continual learning setting of deep neural networks by enforcing the tasks to share the same distribution in the embedding space. We further demonstrate that our model can address the challenges of domain adaptation in the continual learning setting.
We demonstrate that despite major differences, problems within the above learning scenarios can be tackled through learning an intermediate embedding space.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
Telegram
attach 📎
🎯 Deep Learning and Neural Networks Symposium and Workshop
👨🏻🎓 Speaker introduction:
Dr. Reza Ghaderi,
Shahid Beheshti University
Title: Solving cognitive science problems with neural networks
Abstract: This talk gives a brief history of an important part of the mathematical problem-solving techniques that are related to their representation branch and then describes it in neural networks and by introducing the different steps of problems in cognitive science, the use of neural networks in their representation will be described. This description is based on the systematic view of two main parts, namely representation, and optimization. It is hoped that this talk without going into details will enable listeners to use neural network tools in research and problem-solving.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
👨🏻🎓 Speaker introduction:
Dr. Reza Ghaderi,
Shahid Beheshti University
Title: Solving cognitive science problems with neural networks
Abstract: This talk gives a brief history of an important part of the mathematical problem-solving techniques that are related to their representation branch and then describes it in neural networks and by introducing the different steps of problems in cognitive science, the use of neural networks in their representation will be described. This description is based on the systematic view of two main parts, namely representation, and optimization. It is hoped that this talk without going into details will enable listeners to use neural network tools in research and problem-solving.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
Telegram
attach 📎
🎯 Deep Learning and Neural Networks Symposium and Workshop
👨🏻🎓 Speaker introduction:
Prof. Reza Ebrahimpour,
Shahid Rajaee University
Title: Speed accuracy tradeoff explains the neural mechanism of social decision making
Abstract: Confidence could play a vital role in group decision making. Member’s confidence has major impact on the final decision of the group. the neural mechanism of confidence formation and decision making in the isolated situation has been studied extensively in past decades. Computational models were able to successfully explain how confidence forms and how this variable is related to the other behavioral statistics such as accuracy and reaction time. Yet, these questions are still remained to be unanswered in social decision making. Using a multidisciplinary approach, we studied the speed-accuracy tradeoff regime in social decision making to address this gap. Subjects required to decide about motion direction of random dotes while they were paired with computer generated partners. Although, in social decision making, subjects showed increase in confidence and decrease in reaction time, their accuracy remained unchanged. This phenomenon is hardly explainable by computational models in the isolated decision making. Using a modified neural attractor network, we found that confidence of partner could act as a top-down current derived from Prefrontal cortex and toward the decision making area of the brain (Centro-Parietal). The model could not only explain the speed-accuracy tradeoff but could also explain the variation of confidence observed behavioral data. EEG and Eye data also support our computational model where both data suggest that confidence coding would be altered in social situations in a way that our model predict. The finding of this study could enhance our understating regarding confidence formation in the social decision making context.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
👨🏻🎓 Speaker introduction:
Prof. Reza Ebrahimpour,
Shahid Rajaee University
Title: Speed accuracy tradeoff explains the neural mechanism of social decision making
Abstract: Confidence could play a vital role in group decision making. Member’s confidence has major impact on the final decision of the group. the neural mechanism of confidence formation and decision making in the isolated situation has been studied extensively in past decades. Computational models were able to successfully explain how confidence forms and how this variable is related to the other behavioral statistics such as accuracy and reaction time. Yet, these questions are still remained to be unanswered in social decision making. Using a multidisciplinary approach, we studied the speed-accuracy tradeoff regime in social decision making to address this gap. Subjects required to decide about motion direction of random dotes while they were paired with computer generated partners. Although, in social decision making, subjects showed increase in confidence and decrease in reaction time, their accuracy remained unchanged. This phenomenon is hardly explainable by computational models in the isolated decision making. Using a modified neural attractor network, we found that confidence of partner could act as a top-down current derived from Prefrontal cortex and toward the decision making area of the brain (Centro-Parietal). The model could not only explain the speed-accuracy tradeoff but could also explain the variation of confidence observed behavioral data. EEG and Eye data also support our computational model where both data suggest that confidence coding would be altered in social situations in a way that our model predict. The finding of this study could enhance our understating regarding confidence formation in the social decision making context.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
Telegram
attach 📎
🎯 Deep Learning and Neural Networks Symposium and Workshop
👨🏻🎓 Speaker introduction:
Dr. Soheil Kolouri,
Research Scientist and Principal Investigator at HRL Laboratories, Malibu, California
Title: Deep Generative Modeling via Wasserstein Distances
Abstract: Deep generative models have become a cornerstone of modern machine learning. The celebrated generative adversarial networks (GANs) have notably contributed to the recent success of these models. However, GANs are also known to be notoriously difficult to optimize, and they are often not stable. Probability metrics, on the other hand, have proven themselves as a reliable alternative to adversarial networks, and provide a better geometric understanding of the problem. In this talk, I will focus on Wasserstein (GSW) distances, which emerge from the optimal transportation problem, discuss their limitations, and introduce Generalized Sliced Wasserstein distances as a remedy to alleviate some of these limitations. I will then review various applications of the GSW in deep generative modeling and transfer learning.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
👨🏻🎓 Speaker introduction:
Dr. Soheil Kolouri,
Research Scientist and Principal Investigator at HRL Laboratories, Malibu, California
Title: Deep Generative Modeling via Wasserstein Distances
Abstract: Deep generative models have become a cornerstone of modern machine learning. The celebrated generative adversarial networks (GANs) have notably contributed to the recent success of these models. However, GANs are also known to be notoriously difficult to optimize, and they are often not stable. Probability metrics, on the other hand, have proven themselves as a reliable alternative to adversarial networks, and provide a better geometric understanding of the problem. In this talk, I will focus on Wasserstein (GSW) distances, which emerge from the optimal transportation problem, discuss their limitations, and introduce Generalized Sliced Wasserstein distances as a remedy to alleviate some of these limitations. I will then review various applications of the GSW in deep generative modeling and transfer learning.
⭕️ Check our website for more information
⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy
📢 @LoopAcademy
📢 @CMPLab
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
Telegram
attach 📎
📣 معرفی شرکت سامان سلامت پژوه
🩺 حوزه ی فعالیت: بهداشت و سلامت
👨🏻🏫 سخنران: مهندس مهیار هوربخت
🎯 از حامیان کارگاه و سمپوزیم یادگیری عمیق و شبکه های عصبی
📢 برای کسب اطلاعات بیشتر، برنامه برگزاری، معرفی سخنرانان و ... به کانال های تلگرامی زیر مراجعه فرمایید:
@LoopAcademy
@CMPLab
☎️ 021 - 2591 7391
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
🩺 حوزه ی فعالیت: بهداشت و سلامت
👨🏻🏫 سخنران: مهندس مهیار هوربخت
🎯 از حامیان کارگاه و سمپوزیم یادگیری عمیق و شبکه های عصبی
📢 برای کسب اطلاعات بیشتر، برنامه برگزاری، معرفی سخنرانان و ... به کانال های تلگرامی زیر مراجعه فرمایید:
@LoopAcademy
@CMPLab
☎️ 021 - 2591 7391
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
🧠 محل برگزاری و زمان کارگاه یادگیری عمیق و شبکه های عصبی
(Deep Learning and Neural Networks)
📅 تاریخ برگزاری کارگاه: 3 الی 5 اسفند ماه 98.
🕗 ساعت پذیرش: 8 الی 8.5 صبح روز شنبه 3 اسفند ماه.
🏛 محل برگزاری: تالار دانشکده مهندسی برق دانشگاه شهید بهشتی.
📢 برای کسب اطلاعات بیشتر، برنامه برگزاری، معرفی مدرسان و ... به کانال های تلگرامی زیر مراجعه فرمایید:
@LoopAcademy
@CMPLab
☎️ 021-2591 7391
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
(Deep Learning and Neural Networks)
📅 تاریخ برگزاری کارگاه: 3 الی 5 اسفند ماه 98.
🕗 ساعت پذیرش: 8 الی 8.5 صبح روز شنبه 3 اسفند ماه.
🏛 محل برگزاری: تالار دانشکده مهندسی برق دانشگاه شهید بهشتی.
📢 برای کسب اطلاعات بیشتر، برنامه برگزاری، معرفی مدرسان و ... به کانال های تلگرامی زیر مراجعه فرمایید:
@LoopAcademy
@CMPLab
☎️ 021-2591 7391
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
Loop Academy | آکادمیِ لوپ
📣 معرفی سخنرانان سمپوزیم یادگیری عمیق و شبکه های عصبی (Deep Learning and Neural Networks) ⚙️ برگزار کنندگان: پژوهشکده علوم شناختی و مغز دانشگاه شهید بهشتی و آکادمی لوپ. 📅 تاریخ برگزاری کارگاه: 3 الی 5 اسفند ماه 98. 📅 تاریخ برگزاری سمپوزیم: 6 الی 8 اسفند…
⏳ثبت نام سمپوزیم یادگیری عمیق و شبکه های عصبی تنها دو روز دیگر به علت اختلالات شبکه‌ای که در روز های گذشته رخ داده بود تمدید شد.
⭕️ برای مشاهده جزئیات بیشتر و ثبت نام روی این لینک کلیک کنید.
⚠️ ظرفیت این رویداد رو به اتمام است و به هیچ عنوان افزایش ظرفیت نخواهیم داشت بنابراین خواهشمند است علاقمندان به شرکت در این سمپوزیم هرچه سریعتر ثبت نام بفرمایند.
📢 برای کسب اطلاعات بیشتر، برنامه برگزاری، معرفی مدرسان و ... به کانال های تلگرامی زیر مراجعه فرمایید:
@LoopAcademy
@CMPLab
☎️ 021-2591 7391
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
⭕️ برای مشاهده جزئیات بیشتر و ثبت نام روی این لینک کلیک کنید.
⚠️ ظرفیت این رویداد رو به اتمام است و به هیچ عنوان افزایش ظرفیت نخواهیم داشت بنابراین خواهشمند است علاقمندان به شرکت در این سمپوزیم هرچه سریعتر ثبت نام بفرمایند.
📢 برای کسب اطلاعات بیشتر، برنامه برگزاری، معرفی مدرسان و ... به کانال های تلگرامی زیر مراجعه فرمایید:
@LoopAcademy
@CMPLab
☎️ 021-2591 7391
🌐 www.loopacademy.ir
🌐 www.cmplab.ir
برای اتصال به اینترنت مراحل ذیل را انجام دهید.
۱. به وای فای sbu با رمز 123456YHN وصل شوید.
۲. در لینک زیر شماره موبایل خودتان و گذرواژه sbu312 را وارد کنید تا یک پیامک حاوی اکانت اینترنت برای شما ارسال شود.
https://id.sbu.ac.ir/Account/Guests
۳. اکانت دریافتی را در لینک زیر وارد کنید.
www.hotspot.sbu.ac.ir
این شناسه از روز 3 ام تا 8ام اسفندماه فعال خواهد بود
مرکز فناوری اطلاعات و ارتباطات
دانشگاه شهید بهشتی
۱. به وای فای sbu با رمز 123456YHN وصل شوید.
۲. در لینک زیر شماره موبایل خودتان و گذرواژه sbu312 را وارد کنید تا یک پیامک حاوی اکانت اینترنت برای شما ارسال شود.
https://id.sbu.ac.ir/Account/Guests
۳. اکانت دریافتی را در لینک زیر وارد کنید.
www.hotspot.sbu.ac.ir
این شناسه از روز 3 ام تا 8ام اسفندماه فعال خواهد بود
مرکز فناوری اطلاعات و ارتباطات
دانشگاه شهید بهشتی
با سلام و احترام،
با توجه به اعلام وزارت بهداشت مبنی بر تعطیلی فعالیت های آموزشی دانشگاه ها به منظور جلوگیری از شیوع بیماری کرونا کارگاه و سمپوزیم یادگیری عمیق و شبکه های عصبی لغو شده و به تاریخ دیگری موکول میشود.
تاریخ برگزاری مجدد از طریق سایت و کانال های ما اطلاع رسانی خواهد شد.
@Cmplab
@LoopAcademy
http://www.cmplab.ir
http://www.loopacademy.ir
با توجه به اعلام وزارت بهداشت مبنی بر تعطیلی فعالیت های آموزشی دانشگاه ها به منظور جلوگیری از شیوع بیماری کرونا کارگاه و سمپوزیم یادگیری عمیق و شبکه های عصبی لغو شده و به تاریخ دیگری موکول میشود.
تاریخ برگزاری مجدد از طریق سایت و کانال های ما اطلاع رسانی خواهد شد.
@Cmplab
@LoopAcademy
http://www.cmplab.ir
http://www.loopacademy.ir
📬 فرا رسیدن نوروز باستانی را تبریک عرض می‌نماییم.
❗️ با توجه به شرایط موجود تصمیم بر آن شد که آکادمی لوپ یکی از دوره های محبوب را که در رأی گیری زیر بیشترین رأی را بیاورد، این بار بدون محدودیت ظرفیت به صورت رایگان برای اعضای تلگرام در غالب آموزش مجازی با شما همراهی کند ...
🦠 امید است که با همدلی و رعایت نکات لازم به از بین بردن ویروس covid-19 کمک کنیم.
#در_خانه_بمانیم
❌ منتظر خبر‌های تکمیلی باشید.
@LoopAcademy
❗️ با توجه به شرایط موجود تصمیم بر آن شد که آکادمی لوپ یکی از دوره های محبوب را که در رأی گیری زیر بیشترین رأی را بیاورد، این بار بدون محدودیت ظرفیت به صورت رایگان برای اعضای تلگرام در غالب آموزش مجازی با شما همراهی کند ...
🦠 امید است که با همدلی و رعایت نکات لازم به از بین بردن ویروس covid-19 کمک کنیم.
#در_خانه_بمانیم
❌ منتظر خبر‌های تکمیلی باشید.
@LoopAcademy
تمایل دارید کدام آموزش مجازی به صورت رایگان ویژه اعضای تلگرام ارائه شود؟
anonymous poll
برنامه نویسی Scala و Apache Spark – 86
👍👍👍👍👍👍👍 53%
برنامه نویسی Julia برای Data Science – 63
👍👍👍👍👍 39%
یادگیری HCI با رویکرد طراحی تجربه کاربری – 14
👍 9%
👥 163 people voted so far. Poll closed.
anonymous poll
برنامه نویسی Scala و Apache Spark – 86
👍👍👍👍👍👍👍 53%
برنامه نویسی Julia برای Data Science – 63
👍👍👍👍👍 39%
یادگیری HCI با رویکرد طراحی تجربه کاربری – 14
👍 9%
👥 163 people voted so far. Poll closed.
🕛 تنها تا ساعت 12:00 امشب فرصت برای رأی دادن به دوره مورد علاقه تان باقی است.
📣 دوره ای که بیشترین رأی را بیاورد آموزش مجازی آن به صورت رایگان ویژه تمام اعضای تلگرام قرار می گیرد.
⭕️ نام دوره ها:
1⃣ برنامه نویسی Scala و Apache Spark
2⃣ برنامه نویسی Julia برای Data Science
3⃣ یادگیری HCI با رویکرد طراحی تجربه کاربری
@LoopAcademy
📣 دوره ای که بیشترین رأی را بیاورد آموزش مجازی آن به صورت رایگان ویژه تمام اعضای تلگرام قرار می گیرد.
⭕️ نام دوره ها:
1⃣ برنامه نویسی Scala و Apache Spark
2⃣ برنامه نویسی Julia برای Data Science
3⃣ یادگیری HCI با رویکرد طراحی تجربه کاربری
@LoopAcademy
Media is too big
VIEW IN TELEGRAM
👨🏻💻 آموزش مجازی دوره برنامه نویسی Scala و Apache Spark
📖 جلسه اول: معرفی دوره، نصب برنامه های مورد نیاز و آموزش برنامه نویسی مقدماتی Scala
📦 لینک دانلود فایل فشرده این ویدئو
🕓 مدت زمان این جلسه: یک ساعت و چهل و پنج دقیقه
🎥 کیفیت فیلم: 720p
📎 لینک دانلود JDK
📎 لینک دانلود Intellij
📎 لینک دانلود Apache Spark
📎 لینک دانلود winutils
📢 کانال آکادمی لوپ:
@LoopAcademy
📖 جلسه اول: معرفی دوره، نصب برنامه های مورد نیاز و آموزش برنامه نویسی مقدماتی Scala
📦 لینک دانلود فایل فشرده این ویدئو
🕓 مدت زمان این جلسه: یک ساعت و چهل و پنج دقیقه
🎥 کیفیت فیلم: 720p
📎 لینک دانلود JDK
📎 لینک دانلود Intellij
📎 لینک دانلود Apache Spark
📎 لینک دانلود winutils
📢 کانال آکادمی لوپ:
@LoopAcademy
Media is too big
VIEW IN TELEGRAM
👨🏻💻 آموزش مجازی دوره برنامه نویسی Scala و Apache Spark
📖 جلسه دوم: معرفی Spark SQL و آموزش دیتافریم ها.
📦 لینک دانلود فایل فشرده این ویدئو
🕓 مدت زمان این جلسه: یک ساعت و یازده دقیقه
🎥 کیفیت فیلم: 720p
📢 کانال آکادمی لوپ:
@LoopAcademy
📖 جلسه دوم: معرفی Spark SQL و آموزش دیتافریم ها.
📦 لینک دانلود فایل فشرده این ویدئو
🕓 مدت زمان این جلسه: یک ساعت و یازده دقیقه
🎥 کیفیت فیلم: 720p
📢 کانال آکادمی لوپ:
@LoopAcademy
📣📣📣
انتظار ها به پایان رسید!!!!
با هماهنگی های انجام شده بین پژوهشکده علوم شناختی و مغز دانشگاه شهید بهشتی و آکادمی لوپ، سمپوزیم یادگیری عمیق و شبکه های عصبی به صورت آنلاین برگزار خواهد. تمامی دوستانی که قبلا در سمپوزیم ثبت نام کرده اند برای اطلاع از جزئیات برگزاری میتوانند به گروه این برنامه اضافه شوند تا در جریان نحوه برگزاری آنلاین قرار گیرند.
📆تاریخ: ۲ الی ۴ اردیبهشت
برای ثبت نام میتوانید از این لینک استفاده کنید.
🔴 با ما همراه باشید
@LoopAcademy
@Cmplab
http://www.loopacademy.ir
http://www.cmplab.ir
انتظار ها به پایان رسید!!!!
با هماهنگی های انجام شده بین پژوهشکده علوم شناختی و مغز دانشگاه شهید بهشتی و آکادمی لوپ، سمپوزیم یادگیری عمیق و شبکه های عصبی به صورت آنلاین برگزار خواهد. تمامی دوستانی که قبلا در سمپوزیم ثبت نام کرده اند برای اطلاع از جزئیات برگزاری میتوانند به گروه این برنامه اضافه شوند تا در جریان نحوه برگزاری آنلاین قرار گیرند.
📆تاریخ: ۲ الی ۴ اردیبهشت
برای ثبت نام میتوانید از این لینک استفاده کنید.
🔴 با ما همراه باشید
@LoopAcademy
@Cmplab
http://www.loopacademy.ir
http://www.cmplab.ir