Книга Data Science Essentials in Python
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Why Going from Implementing Q-learning to Deep Q-learning Can Be Difficult
🔗 Why Going from Implementing Q-learning to Deep Q-learning Can Be Difficult
3 Questions I was Afraid to Ask (and my Tensorflow 2.0 Template)
🔗 Why Going from Implementing Q-learning to Deep Q-learning Can Be Difficult
3 Questions I was Afraid to Ask (and my Tensorflow 2.0 Template)
Medium
Why Going from Implementing Q-learning to Deep Q-learning Can Be Difficult
3 Questions I was Afraid to Ask (and my Tensorflow 2.0 Template)
Learning by Cheating
https://www.youtube.com/watch?v=u9ZCxxD-UUw&feature=youtu.be
https://arxiv.org/abs/1912.12294
🎥 Learning by Cheating
👁 2 раз ⏳ 259 сек.
https://www.youtube.com/watch?v=u9ZCxxD-UUw&feature=youtu.be
https://arxiv.org/abs/1912.12294
🎥 Learning by Cheating
👁 2 раз ⏳ 259 сек.
Learning by Cheating
Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl
Conference on Robot Learning (CoRL 2019)
Code: https://github.com/dianchen96/LearningByCheatingYouTube
Learning by Cheating
Learning by Cheating Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl Conference on Robot Learning (CoRL 2019) Paper: https://arxiv.org/abs/1912.122...
These Natural Images Fool Neural Networks
🔗 These Natural Images Fool Neural Networks
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers Their blog post on training a neural network is available here: https://www.wandb.com/articles/mnist 📝 The paper "Natural Adversarial Examples" and its dataset are available here: https://arxiv.org/abs/1907.07174 https://github.com/hendrycks/natural-adv-examples Andrej Karpathy's image classifier: https://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html 🙏 We would like to thank our generous Patreon
🔗 These Natural Images Fool Neural Networks
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers Their blog post on training a neural network is available here: https://www.wandb.com/articles/mnist 📝 The paper "Natural Adversarial Examples" and its dataset are available here: https://arxiv.org/abs/1907.07174 https://github.com/hendrycks/natural-adv-examples Andrej Karpathy's image classifier: https://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html 🙏 We would like to thank our generous Patreon
YouTube
These Natural Images Fool Neural Networks (And Maybe You Too)
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers
Their blog post on training a neural network is available here: https://www.wandb.com/articles/mnist
📝 The paper "Natural Adversarial Examples" and its dataset…
Their blog post on training a neural network is available here: https://www.wandb.com/articles/mnist
📝 The paper "Natural Adversarial Examples" and its dataset…
2019 Artificial Intelligence Breakthrough
https://www.google.com/amp/s/www.forbes.com/sites/tomtaulli/2019/12/07/this-years-ai-artificial-intelligence-breakthroughs/amp/
🔗 This Year’s AI (Artificial Intelligence) Breakthroughs
Yes, the innovation is continuing at a furious pace.
https://www.google.com/amp/s/www.forbes.com/sites/tomtaulli/2019/12/07/this-years-ai-artificial-intelligence-breakthroughs/amp/
🔗 This Year’s AI (Artificial Intelligence) Breakthroughs
Yes, the innovation is continuing at a furious pace.
Forbes
This Year’s AI (Artificial Intelligence) Breakthroughs
Yes, the innovation is continuing at a furious pace.
This Insect Has The Only Mechanical Gears Ever Found in Nature
🔗 This Insect Has The Only Mechanical Gears Ever Found in Nature
The small hopping insect Issus coleoptratus uses toothed gears on its joints to precisely synchronize the kicks of its hind legs as it jumps forward
🔗 This Insect Has The Only Mechanical Gears Ever Found in Nature
The small hopping insect Issus coleoptratus uses toothed gears on its joints to precisely synchronize the kicks of its hind legs as it jumps forward
Smithsonian Magazine
This Insect Has The Only Mechanical Gears Ever Found in Nature
The small hopping insect Issus coleoptratus uses toothed gears on its joints to precisely synchronize the kicks of its hind legs as it jumps forward
Using Meta-Learning to Train Agents to Learn Generic Concepts
🔗 Using Meta-Learning to Train Agents to Learn Generic Concepts
During this holiday season, I am revisiting some of the most important AI papers of the last few months.
🔗 Using Meta-Learning to Train Agents to Learn Generic Concepts
During this holiday season, I am revisiting some of the most important AI papers of the last few months.
Medium
Using Meta-Learning to Train Agents to Learn Generic Concepts
During this holiday season, I am revisiting some of the most important AI papers of the last few months.
Closet Data Scientists — Who Are They?
🔗 Closet Data Scientists — Who Are They?
The unassuming next generation of Data Scientists
🔗 Closet Data Scientists — Who Are They?
The unassuming next generation of Data Scientists
Medium
Closet Data Scientists — Who Are They?
The unassuming next generation of Data Scientists
Is Deep Learning the Future of Medical Decision Making?
https://thegradient.pub/is-deep-learning-the-future-of-medical-decision-making/
🔗 Is Deep Learning the Future of Medical Decision Making?
Healthcare is often spoken of as a field that is on the verge of an AI revolution. Big names in AI such as Google DeepMind, publicise their efforts in healthcare, claiming that “AI is poised to transform medicine.” But how impactful has AI been so far? Have we really identified
https://thegradient.pub/is-deep-learning-the-future-of-medical-decision-making/
🔗 Is Deep Learning the Future of Medical Decision Making?
Healthcare is often spoken of as a field that is on the verge of an AI revolution. Big names in AI such as Google DeepMind, publicise their efforts in healthcare, claiming that “AI is poised to transform medicine.” But how impactful has AI been so far? Have we really identified
The Gradient
Is Deep Learning the Future of Medical Decision Making?
Healthcare is often spoken of as a field that is on the verge of an AI revolution. Big names in AI such as Google DeepMind [https://deepmind.com/applied/deepmind-health/], publicise their efforts in healthcare, claiming that “AI is poised to transform medicine.…
ML for Optimization Problems | Qingchen Wang | Kaggle Days
🔗 ML for Optimization Problems | Qingchen Wang | Kaggle Days
Kaggle Days China edition was held on October 19-20 at Damei Center, Beijing. More than 400 data scientists and enthusiasts gathered to learn, make friends, and compete in a full-day offline competition. Kaggle Days is produced by LogicAI and Kaggle. About LogicAI: LogicAI is a boutique Data Science consultancy company owned by Kaggle fans and Grandmasters. As a global company, they do custom end-to-end AI and Data Science development projects as well as trainings for C-level management and tech teams.
🔗 ML for Optimization Problems | Qingchen Wang | Kaggle Days
Kaggle Days China edition was held on October 19-20 at Damei Center, Beijing. More than 400 data scientists and enthusiasts gathered to learn, make friends, and compete in a full-day offline competition. Kaggle Days is produced by LogicAI and Kaggle. About LogicAI: LogicAI is a boutique Data Science consultancy company owned by Kaggle fans and Grandmasters. As a global company, they do custom end-to-end AI and Data Science development projects as well as trainings for C-level management and tech teams.
YouTube
ML for Optimization Problems | Qingchen Wang | Kaggle Days
Kaggle Days China edition was held on October 19-20 at Damei Center, Beijing.
More than 400 data scientists and enthusiasts gathered to learn, make friends, and compete in a full-day offline competition.
Kaggle Days is produced by LogicAI and Kaggle.
About…
More than 400 data scientists and enthusiasts gathered to learn, make friends, and compete in a full-day offline competition.
Kaggle Days is produced by LogicAI and Kaggle.
About…
"Extreme Relative Pose Network under Hybrid Representations"
https://github.com/SimingYan/Hybrid_Relative_Pose
Article: https://arxiv.org/abs/1912.11695
🔗 SimingYan/Hybrid_Relative_Pose
Implementation of Paper "Extreme Relative Pose Network under Hybrid Representations" - SimingYan/Hybrid_Relative_Pose
https://github.com/SimingYan/Hybrid_Relative_Pose
Article: https://arxiv.org/abs/1912.11695
🔗 SimingYan/Hybrid_Relative_Pose
Implementation of Paper "Extreme Relative Pose Network under Hybrid Representations" - SimingYan/Hybrid_Relative_Pose
GitHub
SimingYan/Hybrid_Relative_Pose
Implementation of Paper "Extreme Relative Pose Network under Hybrid Representations" - SimingYan/Hybrid_Relative_Pose
Machine Learning with Spark
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📝 Pentreath - Machine Learning with Spark.pdf - 💾4 909 606
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
📝 Pentreath - Machine Learning with Spark.pdf - 💾4 909 606
Tensorflow — A deep learning framework
🔗 Tensorflow — A deep learning framework
Learn and do hands-on Tensorflow and get prepped to solve big problems
🔗 Tensorflow — A deep learning framework
Learn and do hands-on Tensorflow and get prepped to solve big problems
Medium
Tensorflow — A deep learning framework
Learn and do hands-on Tensorflow and get prepped to solve big problems
🎥 Story of Alan Turing Prize Winner Yoshua Bengio
👁 1 раз ⏳ 1560 сек.
👁 1 раз ⏳ 1560 сек.
Yoshua Bengio OC FRSC is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning
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Story of Alan Turing Prize Winner Yoshua Bengio
Yoshua Bengio OC FRSC is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning
Please Share , Like and Subscribe the…
Please Share , Like and Subscribe the…
Macaw: A conversational bot that enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration
https://www.profillic.com/paper/arxiv:1912.08904
🔗 Macaw: An Extensible Conversational Information Seeking Platform: Model and Code - Profillic
Click To Get Model/Code. Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. Macaw is distributed under the MIT License.
https://www.profillic.com/paper/arxiv:1912.08904
🔗 Macaw: An Extensible Conversational Information Seeking Platform: Model and Code - Profillic
Click To Get Model/Code. Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. Macaw is distributed under the MIT License.
CatalyzeX
Macaw: An Extensible Conversational Information Seeking Platform: Paper and Code
Macaw: An Extensible Conversational Information Seeking Platform. Click To Get Model/Code. Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools,…
Теория вероятности. Математическая статистика.
Лекция 1. Основные понятия теории вероятности
Лекция 2. Случайные величины и их характеристики
Лекция 3. Статистические гипотезы. Динамика процессов
Лекция 4. Направления теории случайных процессов
Лекция 5. Марковские случайные процессы
Лекция 6. Теория массового обслуживания
Лекция 7. Прогнозирование случайных процесов
#video #math
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Лекция 1. Основные понятия теории вероятности
Лекция 2. Случайные величины и их характеристики
Лекция 3. Статистические гипотезы. Динамика процессов
Лекция 4. Направления теории случайных процессов
Лекция 5. Марковские случайные процессы
Лекция 6. Теория массового обслуживания
Лекция 7. Прогнозирование случайных процесов
#video #math
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
🔗 Открыть в Telegram
🎥 Untitled
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🎥 Untitled
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🎥 Untitled
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Robot development with Jupyter
Wolf Vollprecht : https://medium.com/@wolfv/robot-development-with-jupyter-ddae16d4e688
🔗 Robot development with Jupyter
This post shows available tools to build browser based, advanced visualizations in Jupyter Notebooks for ROS and standalone web apps using
Wolf Vollprecht : https://medium.com/@wolfv/robot-development-with-jupyter-ddae16d4e688
🔗 Robot development with Jupyter
This post shows available tools to build browser based, advanced visualizations in Jupyter Notebooks for ROS and standalone web apps using
Medium
Robot development with Jupyter
This post shows available tools to build browser based, advanced visualizations in Jupyter Notebooks for ROS and standalone web apps using