❇️ 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
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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…
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💠 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. 😊👍🙏🌺
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❇️ 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
❇️ From Physics to Finance: My First Year in Industry
💠 by: Shoresh Shafei, Data Scientist at Google
🌍 https://www.linkedin.com/pulse/from-physics-finance-my-first-year-industry-shoresh-shafei/
💠 by: Shoresh Shafei, Data Scientist at Google
🌍 https://www.linkedin.com/pulse/from-physics-finance-my-first-year-industry-shoresh-shafei/
LinkedIn
From Physics to Finance: My First Year in Industry
Many graduate and postdocs pursue non-research careers in Industry. This is the story of one of them.