❇️ Medical Augmented Reality (AR) Summer School 2019
❇️ Application open now
From 5 to 16 August 2019, Balgrist Campus will carry out the Medical AR Summer School. This exciting event will teach a new generation of scientists the basics, challenges and recent advantages of Augmented Reality in medicine. The aim is to strengthen the connection between clinic, research and industry.
🌍 Apply here: https://www.medicalaugmentedreality.org
In collaboration with:
🔶 Technische Universität München
🔶 Universität Zürich | University of Zurich (HMZ)
🔶 The Johns Hopkins University
🔶 Comerge AG
❇️ Application open now
From 5 to 16 August 2019, Balgrist Campus will carry out the Medical AR Summer School. This exciting event will teach a new generation of scientists the basics, challenges and recent advantages of Augmented Reality in medicine. The aim is to strengthen the connection between clinic, research and industry.
🌍 Apply here: https://www.medicalaugmentedreality.org
In collaboration with:
🔶 Technische Universität München
🔶 Universität Zürich | University of Zurich (HMZ)
🔶 The Johns Hopkins University
🔶 Comerge AG
FDS.pdf
2.4 MB
#data_science_resources
❇️ پایه های علم داده
❇️ Foundations of Data Science
🌍 https://www.microsoft.com/en-us/research/video/foundations-of-ds/?OCID=msr_project_datascibook_tw
❇️ پایه های علم داده
❇️ Foundations of Data Science
🌍 https://www.microsoft.com/en-us/research/video/foundations-of-ds/?OCID=msr_project_datascibook_tw
❇️ هرس کردن شبکه های عصبی عمیق برای کوچکتر و سریعتر کردن آنها
💎 Getting faster/smaller networks is important for running these deep learning networks on mobile devices.
❇️ Pruning neural networks is an old idea going back to 1990 (with Yan Lecun’s optimal brain damage work) and before. The idea is that among the many parameters in the network, some are redundant and don’t contribute a lot to the output.
If you could rank the neurons in the network according to how much they contribute, you could then remove the low ranking neurons from the network, resulting in a smaller and faster network.
🌍 https://jacobgil.github.io/deeplearning/pruning-deep-learning?fbclid=IwAR3ZSMpUenU0XzMKhcexEh8rRZvdpImAg4xp93syrjkq3SbHaKPgq0OKoAE
💎 Getting faster/smaller networks is important for running these deep learning networks on mobile devices.
❇️ Pruning neural networks is an old idea going back to 1990 (with Yan Lecun’s optimal brain damage work) and before. The idea is that among the many parameters in the network, some are redundant and don’t contribute a lot to the output.
If you could rank the neurons in the network according to how much they contribute, you could then remove the low ranking neurons from the network, resulting in a smaller and faster network.
🌍 https://jacobgil.github.io/deeplearning/pruning-deep-learning?fbclid=IwAR3ZSMpUenU0XzMKhcexEh8rRZvdpImAg4xp93syrjkq3SbHaKPgq0OKoAE
Jacob's Computer Vision and Machine Learning blog
Pruning deep neural networks to make them fast and small
My PyTorch implementation of [1611.06440 Pruning Convolutional Neural Networks for Resource Efficient Inference].
🎥 حجم این ویدئو کمی زیاد است ولی ارزش دیدن دارد، حتما آن را ببینید بر دیدگاهتان در زندگی و کار علمی تاثیر خواهد گذاشت 👇👇
💎💎لذت دانستن چیزها💎💎
این مستند بسیار پرمحتوا و ارزشمندی ست که در آن ریچارد فیلیپ فاینمن یکی از بزرگترین فیزیکدانان قرن بیستم درباره "لذت دانستن چیزها" صجبت می کند و اینکه خود او چگونه در ارتباطی بسیار نزدیک و عمیق با پدرش به "دانستن چیزها" علاقه مند شده است و
💠 تفاوت دانستن نام چیزی و دانستن خود آن چیز 💠
را دریافته است.
❇️ در اینترنت برای زیرنویس فارسی یا انگلیسی آن جستجوهایی کردم ولی چیزی نیافتم، اگر کسی زیرنویس فارسی یا انگلیسی این مستند را دارد لطفا آن را به اشتراک بگذارد.
💎💎لذت دانستن چیزها💎💎
این مستند بسیار پرمحتوا و ارزشمندی ست که در آن ریچارد فیلیپ فاینمن یکی از بزرگترین فیزیکدانان قرن بیستم درباره "لذت دانستن چیزها" صجبت می کند و اینکه خود او چگونه در ارتباطی بسیار نزدیک و عمیق با پدرش به "دانستن چیزها" علاقه مند شده است و
💠 تفاوت دانستن نام چیزی و دانستن خود آن چیز 💠
را دریافته است.
❇️ در اینترنت برای زیرنویس فارسی یا انگلیسی آن جستجوهایی کردم ولی چیزی نیافتم، اگر کسی زیرنویس فارسی یا انگلیسی این مستند را دارد لطفا آن را به اشتراک بگذارد.
Telegram
attach📎
❇️ در مقاله ای بسیار جالب Samsung AI شبکه عصبی ای را طراحی کرده است که از روی فریم های ویدیوی واقعی صحبت کردن یک فرد (حتی یک شات) یادگیری انجام میدهد و سپس آن را به یک پرتره منتقل میکند و به آن جان می بخشد.
❇️ https://arxiv.org/abs/1905.08233
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
❇️ https://arxiv.org/abs/1905.08233
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Telegram
attach📎
AI in Science & Technology
❇️ در مقاله ای بسیار جالب Samsung AI شبکه عصبی ای را طراحی کرده است که از روی فریم های ویدیوی واقعی صحبت کردن یک فرد (حتی یک شات) یادگیری انجام میدهد و سپس آن را به یک پرتره منتقل میکند و به آن جان می بخشد. ❇️ https://arxiv.org/abs/1905.08233…
This media is not supported in your browser
VIEW IN TELEGRAM
💠 مجموعه مقالاتی دنباله دار و متوالی به زبانی ساده و با بیانی بسیار عالی از وبسایت مدیوم برای کسانی که دوست دارند خیلی سریع با دنیای یادگیری ماشین و مثالهایی واقعی از آن به همراه کد نویسی آشنا بشوند. در انتها در بخش ضمیمه هم منابع بسیار جالب و مفید معرفی شده است.
💠 https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
Roadmap
❇️ Part 1: Why Machine Learning Matters.
The big picture of artificial intelligence and machine learning — past, present, and future.
❇️ Part 2.1: Supervised Learning. Learning with an answer key.
Introducing linear regression, loss functions, overfitting, and gradient descent.
❇️ Part 2.2: Supervised Learning II.
Two methods of classification: logistic regression and SVMs.
❇️ Part 2.3: Supervised Learning III.
Non-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.
❇️ Part 3: Unsupervised Learning.
Clustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD).
❇️ Part 4: Neural Networks & Deep Learning.
Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications.
❇️ Part 5: Reinforcement Learning.
Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.
☯️ Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum.
💠 https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
Roadmap
❇️ Part 1: Why Machine Learning Matters.
The big picture of artificial intelligence and machine learning — past, present, and future.
❇️ Part 2.1: Supervised Learning. Learning with an answer key.
Introducing linear regression, loss functions, overfitting, and gradient descent.
❇️ Part 2.2: Supervised Learning II.
Two methods of classification: logistic regression and SVMs.
❇️ Part 2.3: Supervised Learning III.
Non-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.
❇️ Part 3: Unsupervised Learning.
Clustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD).
❇️ Part 4: Neural Networks & Deep Learning.
Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications.
❇️ Part 5: Reinforcement Learning.
Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.
☯️ Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum.
Medium
A Beginner’s Guide to AI/ML 🤖👶
The ultimate guide to machine learning. Simple, plain-English explanations accompanied by math, code, and real-world examples.
❇️ مصاحبه در دانشگاه استافورد درباره هوش مصنوعی با حضور یووال نوح هراری (نویسنده کتاب انسان خردمند) و فیی-فیی لی (خالق ImageNet)
❇️ ۴ سوال که بر همه ما اثر می گذارد.
More questions than answers were generated during a recent conversation at Stanford University between a pair of Artificial Intelligence giants — Yuval Noah Harari and Fei-Fei Li. Nicholas Thompson, editor in chief of WIRED, moderated the 90-minute conversation in the packed Memorial Auditorium, filled to its 1705-seat capacity.
🌍 https://towardsdatascience.com/yuval-noah-harari-and-fei-fei-li-on-ai-90d9a8686cc5
❇️ ۴ سوال که بر همه ما اثر می گذارد.
More questions than answers were generated during a recent conversation at Stanford University between a pair of Artificial Intelligence giants — Yuval Noah Harari and Fei-Fei Li. Nicholas Thompson, editor in chief of WIRED, moderated the 90-minute conversation in the packed Memorial Auditorium, filled to its 1705-seat capacity.
🌍 https://towardsdatascience.com/yuval-noah-harari-and-fei-fei-li-on-ai-90d9a8686cc5
Medium
Yuval Noah Harari and Fei-Fei Li on Artificial Intelligence: Four Questions that Impact All of Us
Four Questions that Impact Each of Us
Forwarded from TechInsider
چرا هوش مصنوعی هنوز بازارهای مالی دنیا را فتح نکرده است؟
https://www.weforum.org/agenda/2019/02/the-vodka-is-strong-but-the-meat-is-soft-why-ai-hasn-t-taken-over-the-financial-markets-yet
@techinsider_channel
https://www.weforum.org/agenda/2019/02/the-vodka-is-strong-but-the-meat-is-soft-why-ai-hasn-t-taken-over-the-financial-markets-yet
@techinsider_channel
World Economic Forum
Why AI hasn’t taken over the financial markets. Yet
Analysts say the market is dominated by algorithmic trading. They couldn't be more wrong.
❇️ دروس رایگان در سطوح مقدماتی، متوسط و پیشرفته یادگیری ماشین وبسایت Kaggle
❇️ Kaggle FREE elementary, intermediate and advanced ML courses:
💠سطح مقدماتی
Python:
🌍 https://www.kaggle.com/learn/python
Intro to Machine Learning :
🌍 https://www.kaggle.com/learn/intro-to-machine-learning
💠 سطح متوسط
Intermediate Machine Learning:
🌍 https://www.kaggle.com/learn/intermediate-machine-learning
💠 سطح پیشرفته
Machine Learning Explainability:
🌍 https://www.kaggle.com/learn/machine-learning-explainability
💠 همه ۱۱ درس یادگیری ماشین
All 11 ML courses:
🌍 https://www.kaggle.com/learn/overview
❇️ Kaggle FREE elementary, intermediate and advanced ML courses:
💠سطح مقدماتی
Python:
🌍 https://www.kaggle.com/learn/python
Intro to Machine Learning :
🌍 https://www.kaggle.com/learn/intro-to-machine-learning
💠 سطح متوسط
Intermediate Machine Learning:
🌍 https://www.kaggle.com/learn/intermediate-machine-learning
💠 سطح پیشرفته
Machine Learning Explainability:
🌍 https://www.kaggle.com/learn/machine-learning-explainability
💠 همه ۱۱ درس یادگیری ماشین
All 11 ML courses:
🌍 https://www.kaggle.com/learn/overview
💠 Recommended: Introduction to Deep Learning
🌍 https://www.coursera.org/courses?utm_medium=email&utm_source=recommendations&utm_campaign=4EV2EIu5EemdOAf9ya7Q1g
🌍 https://www.coursera.org/courses?utm_medium=email&utm_source=recommendations&utm_campaign=4EV2EIu5EemdOAf9ya7Q1g
Coursera
Top Online Courses and Certifications [2025] | Coursera Learn Online
Find Courses and Certifications from top universities like Yale, Michigan, Stanford, and leading companies like Google and IBM. Join Coursera for free and transform your career with degrees, certificates, Specializations, & MOOCs in data science, computer…
Forwarded from ziSTartup
CB-Insights_AI-Trends-2019-iotreport.pdf
5.7 MB
❇️ How to Accelerate Data Labeling and Deep Learning Training
💠 Thursday, June 27, 2019 · 11:00 AM · Pacific Time (US & Canada)
🌍 https://www.cloudfactory.com/webinar/accelerate-data-labeling-dl-training?utm_medium=email&utm_campaign=CF_ML_Webinar&utm_term=MissingLink&utm_source=ML_email
💠 Thursday, June 27, 2019 · 11:00 AM · Pacific Time (US & Canada)
🌍 https://www.cloudfactory.com/webinar/accelerate-data-labeling-dl-training?utm_medium=email&utm_campaign=CF_ML_Webinar&utm_term=MissingLink&utm_source=ML_email
Cloudfactory
How to Accelerate Data Labeling and DL Training | CloudFactory Webinar
Developing high-performance deep learning models for computer vision requires a strategic combination of people, tools, and processes in pre-production. Watch the webinar to learn how to accelerate data labeling and DL training.
کل کتاب
💠 Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow💎SECOND EDITION💎
Concepts, Tools, and Techniques to Build Intelligent Systems
❇️ by: Aurélien Géron
برای ترجمه پوشش داده شده است.
با تشکر از همه عزیزانی که در این ترجمه شرکت کرده اند. 😊👍🙏🌺
💠 Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow💎SECOND EDITION💎
Concepts, Tools, and Techniques to Build Intelligent Systems
❇️ by: Aurélien Géron
برای ترجمه پوشش داده شده است.
با تشکر از همه عزیزانی که در این ترجمه شرکت کرده اند. 😊👍🙏🌺
This media is not supported in your browser
VIEW IN TELEGRAM
❇️ Argo.ai has released a very cool new dataset called Argoverse which can be used for computer vision/ML research and in particular to advance self-driving cars technology.
The datasets includes:
- Two HD maps with total 290km of mapped roadway (Miami & Pittsburgh)
- 3D tracking annotations for 113 scenes with 11,319 tracked objects
- 327,793 interesting vehicle trajectories from 1000 driving hours which is useful for motion forecasting
- An API to connect the map data with sensor information
Check it out!
📝 Article: https://lnkd.in/dJFeRFK
🚗 Dataset: https://lnkd.in/dqCTwf2
🔤 Github: https://lnkd.in/daay-ze
The datasets includes:
- Two HD maps with total 290km of mapped roadway (Miami & Pittsburgh)
- 3D tracking annotations for 113 scenes with 11,319 tracked objects
- 327,793 interesting vehicle trajectories from 1000 driving hours which is useful for motion forecasting
- An API to connect the map data with sensor information
Check it out!
📝 Article: https://lnkd.in/dJFeRFK
🚗 Dataset: https://lnkd.in/dqCTwf2
🔤 Github: https://lnkd.in/daay-ze
0.pdf
1.1 MB
❇️ Python for Data Analysis (47 pages) 💠 Katia Oleinikو Boston University
❇️ The 3rd YouTube-8M Video Understanding Challenge
💠 Temporal localization of topics within video
🌍 https://www.kaggle.com/c/youtube8m-2019?utm_medium=email&utm_source=intercom&utm_campaign=YT8-comp-mailer
💠 Temporal localization of topics within video
🌍 https://www.kaggle.com/c/youtube8m-2019?utm_medium=email&utm_source=intercom&utm_campaign=YT8-comp-mailer
Kaggle
The 3rd YouTube-8M Video Understanding Challenge
Temporal localization of topics within video