Is Simulating Jelly And Bunnies Possible? 🐰

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/39vhPCn 📝 The paper "Monolith: A Monolithic Pressure-Viscosity-Contact Solver for Strong Two-Way Rigid-Rigid Rigid-Fluid Coupling" is available here: https://ift.tt/3rEAMsT 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Overview Machine Learning (AI: Workshop & Tutorials)

Overview Machine Learning (AI: Workshop & Tutorials) by: Adila Alfa Krisnadhi, Ph.D. Materi: s.id/MateriAIHari2

Machine Learning (AI Workshop & Tutorials)

Machine Learning (AI Workshop & Tutorials) by: Fariz Darari, Ph.D Materi: s.id/MateriAIHari1

Customizable AR face masks - Made with TensorFlow.js

Today on Made with TensorFlow.js we’re joined by Samarth Gulati and Praveen Sinha from India, to hear how they’ve used TensorFlow.js and Facemesh model to create a system that can recreate digital face masks based on cultural events around their country. Hosted by Jason Mayes, Senior Developer Advocate for TensorFlow. Use the #MadeWithTFJS to share your own creations, and we may feature you in our next show! AR Face Filters GitHub → http://goo.gle/3a0yH4a Live Demo → http://goo.gle/2W7MX3c TFJS Facemesh workshop with NodeSchoolSF → https://goo.gle/2WTWZVX TFJS Meetup walk-through → https://goo.gle/2WPIO3Y Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

Overview Machine Learning (AI: Workshop & Tutorials)

Overview Machine Learning (AI: Workshop & Tutorials) by: Adila Alfa Krisnadhi, Ph.D. Materi: s.id/MateriAIHari2

Machine Learning (AI Workshop & Tutorials)

Machine Learning (AI Workshop & Tutorials) by: Fariz Darari, Ph.D Materi: s.id/MateriAIHari1

Live BOT Binomo Trading AI Bynary Option - AI Learning #96

This Video Have Purpose For My Historical Analysis of AI Learning on my Trading Bot Application #binomo#tradingbinomolive#livebinomo#binomoindonesia#tradingbotbinomo

Live BOT Binomo Trading AI Bynary Option - AI Learning #97

This Video Have Purpose For My Historical Analysis of AI Learning on my Trading Bot Application #binomo#tradingbinomolive#livebinomo#binomoindonesia#tradingbotbinomo

AI & Machine Learning Workshop: The Tutorial before your Tutorial - Part 2

#MachineLearningTutorial #AI #MachineLearning #Tutorial #ScienceandTechnology #ArtificialIntelligence #TensorFlow #Keras #SupervisedLearning #NeuralNetworks Checkout out Part 1 of this series: https://youtu.be/poQp5N2flOw Artificial Intelligence and Machine Learning with TensorFlow/Keras is a confusing and sometimes incomprehensible subject to learn on your own. The Google Machine Learning Crash Course is a good tutorial to learn AI/ML if you already have a background on the subject. The purpose of this workshop is the be the tutorial before to take the Google tutorial. I've been there and now I'm ready to pass it forward and share what I've learned. I'm not an expert but I have working code examples that I will use to teach you based on my current level of understanding of the subject. Here is the list of topics explained in this Machine Learning basics video: 1.Machine Learning Pipeline Tools Overview - (2:15) 2. Machine Learning Pipeline Process - (6:10) 3. Machine Learning Work flow - ( 10:20) Like/follow us on Facebook: https://www.facebook.com/Black-Magic-AI-109126344070229 Check out our Web site: https://www.blackmagicai.com/ Background Music Royalty Free background music from Bensound.com.

Greetings Artificio - AI/ML platform predicting wishes and happy holidays 2020 for everyone.

Artificio - AI/ML platform predicting wishes and happy holidays 2020 by using Artificial Intelligence entity extraction model.. Wish everyone Merry Christmas and Happy holidays ahead. Reading text from any document is no more challenge. #artificialintelligence #innovation #ai #ml #digitaltransformation #machinelearning

Intro to AI/ML Course: Machine Learning Theory

Welcome to the second lesson of the Intro to AI/ML Course! In this lesson, we go over the fundamentals of machine learning theory and do a small machine learning project in Python to show how we execute these ideas in code. Here are the notebooks we used for this lesson: Theory: https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/2_ConceptsML.ipynb Coding project: https://github.com/Intro-Course-AI-ML/LessonMaterials/blob/master/projects/1_LinearRegressionProject.ipynb Here is our GitHub repository with the materials for this course: https://github.com/Intro-Course-AI-ML/LessonMaterials. All our materials will be contained here. If you have any questions or concerns, please contact us at ayaanzhaque@gmail.com and viraajreddi@gmail.com. You can also create a GitHub issue in our repository and we will quickly take a look at it

Extracting Training Data from Large Language Models (Paper Explained)

#ai #privacy #tech This paper demonstrates a method to extract verbatim pieces of the training data from a trained language model. Moreover, some of the extracted pieces only appear a handful of times in the dataset. This points to serious security and privacy implications for models like GPT-3. The authors discuss the risks and propose mitigation strategies. OUTLINE: 0:00 - Intro & Overview 9:15 - Personal Data Example 12:30 - Eidetic Memorization & Language Models 19:50 - Adversary's Objective & Outlier Data 24:45 - Ethical Hedgeing 26:55 - Two-Step Method Overview 28:20 - Perplexity Baseline 30:30 - Improvement via Perplexity Ratios 37:25 - Weights for Patterns & Weights for Memorization 43:40 - Analysis of Main Results 1:00:30 - Mitigation Strategies 1:01:40 - Conclusion & Comments Paper: https://ift.tt/2KyBarR Abstract: It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model. We demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model's training data. These extracted examples include (public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs. Our attack is possible even though each of the above sequences are included in just one document in the training data. We comprehensively evaluate our extraction attack to understand the factors that contribute to its success. For example, we find that larger models are more vulnerable than smaller models. We conclude by drawing lessons and discussing possible safeguards for training large language models. Authors: Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, Colin Raffel Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Machine Learning Algorithms | Machine Learning Tutorial | Edureka | Python Rewind - 4

🔥Edureka Python Certification Training: https://www.edureka.co/python-program... This Edureka Live video on 'Machine Learning Algorithms' will help you understand what is an Algorithm and what is Machine and learn how to solve a problem using ML with Hands-on Python Tutorial Playlist: https://goo.gl/WsBpKe Blog Series: http://bit.ly/2sqmP4s 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV -----------------------Edureka Online Training and Certification------------------ 🔵 DevOps Online Training: https://bit.ly/2BPwXf0 🟣 Python Online Training: https://bit.ly/2CQYGN7 🔵 AWS Online Training: https://bit.ly/2ZnbW3s 🟣 RPA Online Training: https://bit.ly/2Zd0ac0 🔵 Data Science Online Training: https://bit.ly/2NCT239 🟣 Big Data Online Training: https://bit.ly/3g8zksu 🔵 Java Online Training: https://bit.ly/31rxJcY 🟣 Selenium Online Training: https://bit.ly/3dIrh43 🔵 PMP Online Training: https://bit.ly/3dJxMTW 🟣 Tableau Online Training: https://bit.ly/3g784KJ -----------------------------------------Edureka Masters Programs--------------------------------------------------- 🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp 🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ 🔵Data Scientist Masters Program: https://bit.ly/2YHaolS 🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv 🔵Machine Learning Engineer Masters Program: https://bit.ly/388NXJi 🟣Business Intelligence Masters Program: https://bit.ly/2BPLtn2 🔵Python Developer Masters Program: https://bit.ly/2Vn7tgb 🟣RPA Developer Masters Program: https://bit.ly/3eHwPNf -----------------------------------------Edureka PGP Courses--------------------------------------------------- 🔵Artificial and Machine Learning PGP: https://bit.ly/2Ziy7b1 🟣CyberSecurity PGP: https://bit.ly/3eHvI0h 🔵Digital Marketing PGP: https://bit.ly/38cqdnz 🟣Big Data Engineering PGP: https://bit.ly/3eTSyBC 🔵Data Science PGP: https://bit.ly/3dIeYV9 🟣Cloud Computing PGP: https://bit.ly/2B9tHLP ------------------------------ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_lea... Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?count... Meetup: https://www.meetup.com/edureka/ #edureka #edurekapython #learnpythonfromscratch #pythonprogramsforpractice #pythonprogramming #pythontutorial #pythonforbeginners #PythonTraining #learnPython #withMe ----------------------- How it Works? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! ---------------------------- About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. ---------------------------- Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. ---------------------------- Who should go for python? Edureka’s Data Science certification course in Python is a good fit for the below professionals: · Programmers, Developers, Technical Leads, Architects · Developers aspiring to be a ‘Machine Learning Engineer' · Analytics Managers who are leading a team of analysts · Business Analysts who want to understand Machine Learning (ML) Techniques · Information Architects who want to gain expertise in Predictive Analytics · 'Python' professionals who want to design automatic predictive models For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll free)

MEMES IS ALL YOU NEED - Deep Learning Meme Review - Episode 2 (Part 1 of 2)

Antonio and I critique the creme de la creme of Deep Learning memes. Music: Sunshower - LATASHÁ Papov - Yung Logos Sunny Days - Anno Domini Beats Trinity - Jeremy Blake Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Crop yield modelling through weather using ML and AI by Dr. K.N. Singh

Crop yield modelling through weather using machine learning and artificial intelligence by Dr. K.N. Singh

Artificial Intelligence vs Machine Learning vs Deep Learning in Telugu

In this video, you will learn the difference between Artificial Intelligence, Machine Learning, and Deep Learning Link: https://medium.com/@sapireddyrahul/artificial-intelligence-vs-machine-learning-vs-deep-learning-bc8736dddf0d

Python Snake AI Tutorial - Reinforcement Learning - Creating The Model (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this fourth and final part we implement the neural network to predict the moves and train it. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! Other helpful tutorials: Part 1: https://youtu.be/PJl4iabBEz0 Anaconda Tutorial: https://youtu.be/9nEh-OXVaNI PyTorch Course: https://youtube.com/playlist?list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4 Snake Pygame: https://youtu.be/--nsd2ZeYvs Code: https://github.com/python-engineer/snake-ai-pytorch More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Rock Paper Scissors | AI | Machine Learning

#artificialintelligence #ai #machinelearning #ml #machine #rockpaperscissors #coding

R-Ladies Amsterdam: Automatic & Explainable Machine Learning in R with H2O by Erin LeDell

Big thanks to our speaker Erin LeDell, Chief Machine Learning Scientist at H2O.ai, founder of R-Ladies Global and Women in Machine Learning and Data Science! In the workshop, Erin will introduce you to the concepts of AutoML within H2O, followed by explainable machine learning. *** Slides: https://github.com/h2oai/h2o-meetups/tree/master/2020_12_17_RLadiesAMS_H2OAutoMLExplain Follow R- Ladies Amsterdam Twitter: https://twitter.com/RLadiesAMS Meetup: https://www.meetup.com/rladies-amsterdam/ LinkedIn: https://www.linkedin.com/company/r-ladies-amsterdam Instagram: https://www.instagram.com/rladiesams/ ***

Python Snake AI Tutorial - Reinforcement Learning - Creating The Agent (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this third part we implement the agent that controls the game. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! Other helpful tutorials: Part 1: https://youtu.be/PJl4iabBEz0 Anaconda Tutorial: https://youtu.be/9nEh-OXVaNI Snake Pygame: https://youtu.be/--nsd2ZeYvs Code: https://github.com/python-engineer/snake-ai-pytorch Python Deque: https://docs.python.org/3/library/collections.html#collections.deque More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Python Snake AI Tutorial - Reinforcement Learning - Creating The Model (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this fourth and final part we implement the neural network to predict the moves and train it. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! Other helpful tutorials: Part 1: https://youtu.be/PJl4iabBEz0 Anaconda Tutorial: https://youtu.be/9nEh-OXVaNI Snake Pygame: https://youtu.be/--nsd2ZeYvs Code: https://github.com/python-engineer/snake-ai-pytorch More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Python Snake AI Tutorial - Reinforcement Learning - Creating The Snake (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this second part we setup the environment and implement the Snake game. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! Other helpful tutorials: Part 1: https://youtu.be/PJl4iabBEz0 Anaconda Tutorial: https://youtu.be/9nEh-OXVaNI Snake Pygame: https://youtu.be/--nsd2ZeYvs Code: https://github.com/python-engineer/snake-ai-pytorch More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Workshop for Artificial intelligence, machine learning, and the importance of AI in schools

Python Snake AI Tutorial - Reinforcement Learning - Creating The Agent (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this second part we setup the environment and implement the Snake game. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! Other helpful tutorials: Part 1: https://youtu.be/PJl4iabBEz0 Anaconda Tutorial: https://youtu.be/9nEh-OXVaNI Snake Pygame: https://youtu.be/--nsd2ZeYvs Code: https://github.com/python-engineer/snake-ai-pytorch Python Deque: https://docs.python.org/3/library/collections.html#collections.deque More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Real-time AR Sudoku solver - Made with TensorFlow.js

Today on Made with TensorFlow.js we’re joined by Chris Greening from the UK, who’s built an augmented reality web app to solve Sudoku puzzles in real-time. Chris breaks down his problem-solving techniques in building a complex app like this, including methods for image processing and character recognition. Hosted by Jason Mayes, Senior Developer Advocate for TensorFlow. Sudoku in-depth how it was made → https://goo.gle/2W9fkxC Sudoku live demo → https://goo.gle/3qNGx7m Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

Painting the Mona Lisa...With Triangles! 📐

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Differentiable Vector Graphics Rasterization for Editing and Learning" is available here: - https://ift.tt/37GT0C9 - https://ift.tt/37EaHm0 The mentioned Mona Lisa genetic algorithm is available here: https://ift.tt/1QLxe1t ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Python Snake AI Tutorial - Reinforcement Learning - Deep Q Learning (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this first part I'll show you the project and teach you some basics about Reinforcement Learning and Deep Q Learning. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Artificial Intelligence- Machine Learning- Deep Learning

Machine learning and deep learning are subfields of AI As a whole, artificial intelligence contains many subfields, including: Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude. A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data. Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition. Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings. Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks. While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly." Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. The type of machine learning from our previous example, called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous datasets fed. Unsupervised learning, another type of machine learning, is the family of machine learning algorithms, which have main uses in pattern detection and descriptive modeling. These algorithms do not have output categories or labels on the data (the model trains with unlabeled data). What Is Deep Learning? Some consider deep learning to be the next frontier of machine learning, the cutting edge of the cutting edge. You may already have experienced the results of an in-depth deep learning program without even realizing it! If you’ve ever watched Netflix, you’ve probably seen its recommendations for what to watch. And some streaming-music services choose songs based on what you’ve listened to in the past or songs you’ve given the thumbs-up to or hit the “like” button for. Both of those capabilities are based on deep learning. Google’s voice recognition and image recognition algorithms also use deep learning. Just as machine learning is considered a type of AI, deep learning is often considered to be a type of machine learning—some call it a subset. While machine learning uses simpler concepts like predictive models, deep learning uses artificial neural networks designed to imitate the way humans think and learn. You may remember from high school biology that the primary cellular component and the main computational element of the human brain is the neuron and that each neural connection is like a small computer. The network of neurons in the brain is responsible for processing all kinds of input: visual, sensory, and so on. With deep learning computer systems, as with machine learning, the input is still fed into them, but the info is often in the form of huge data sets because deep learning systems need a large amount of data to understand it and return accurate results. Then the artificial neural networks ask a series of binary true/false questions based on the data, involving highly complex mathematical calculations, and classify that data based on the answers received. So although both machine and deep learning fall under the general classification of artificial intelligence, and both “learn” from data input, there are some key differences between the two. If you’d like to learn more specifically about deep learning, by the way, you can check out this Introduction to Deep Learning tutorial. It’s also worth learning separately about deep learning with TensorFlow, as TensorFlow is one of the most popular libraries for implementing deep learning.

Python Snake AI Tutorial - Reinforcement Learning - Creating The Snake (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this second part we setup the environment and implement the Snake game. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! Other helpful tutorials: Part 1: https://youtu.be/PJl4iabBEz0 Anaconda Tutorial: https://youtu.be/9nEh-OXVaNI Snake Pygame: https://youtu.be/--nsd2ZeYvs Code: https://github.com/python-engineer/snake-ai-pytorch More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning Scikit-Learn merupakan salah satu library machine learning berbasis Python yang sangat direkomendasikan. Scikit-Learn memenuhi kebutuhan matematika, pengolahan data hingga banyak algoritma ML seperti Regresi, Classification, dan SVM. Jadi jika kalian baru saja memulai belajar AI atau Machine Learning, library ini menjadi pilihan yang tepat untuk dipelajari. Ikuti webinar trial DILo Medan x BISA AI yang akan membahas dari awal penggunaan Scikit-Learn dan beberapa kasusnya secara langsung, bersama: - M. Octaviano Pratama, S.Kom., M.Kom Chief Scientist BISA AI Acara ini akan dilaksanakan pada: Sabtu, 19 Desember 2020 14.00 WIB – Selesai Live dari BISA Tampil dan YouTube BISA AI Daftar acaranya di: bit.ly/dmonline28 --- 📌 Playlist video event workshop, seminar, dan talkshow dari berbagai narasumber mengenai Python, AI, dll di: https://www.youtube.com/playlist?list=PLwEzEX1KA1ngauZlV1lQLk7XSBtifdSgL --- 📌 Lihat event-event selanjutnya di: https://bisa.ai/tampil/event --- Visit our website at : https://bisa.ai Blog at : https://medium.com/bisa-ai Instagram : https://www.instagram.com/bisa.ai/ Contact Person (BISA.AI) : +62-8211-6654-087 for latest info and events

Adversarial machine learning tutorial Ep. 1 เสกหมีแพนด้าให้กลายเป็นแมลงสาบ | โดย AibyNeto

Adversarial machine learning tutorial Ep. 1 เสกหมีแพนด้าให้กลายเป็นแมลงสาบ ช่องทางการติดตาม Neto-san Facebook: AI powered by Neto-san YouTube: AibyNeto #Neuralnetwork #Deeplearning #Machinelearning #Adversarialexamples

Python Snake AI Tutorial - Reinforcement Learning - Deep Q Learning (PyTorch + Pygame)

In this Python Reinforcement Learning Tutorial series we teach an AI to play Snake! We build everything from scratch using Pygame and PyTorch. In this first part I'll show you the project and teach you some basics about Reinforcement Learning and Deep Q Learning. 🪁 Code faster with Kite, AI-powered autocomplete that integrates into VS Code! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only 🚀🚀 Get monthly Python and ML Tips: https://www.python-engineer.com/newsletter/ 🚀🚀 SUPPORT ME ON PATREON: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel! More Resources about Deep Q Learning: https://www.freecodecamp.org/news/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe/ https://www.freecodecamp.org/news/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8/ https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a You can find me here: Website: https://www.python-engineer.com Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Music: https://www.bensound.com/ #Python

Artificial Intelligence- Machine Learning- Deep Learning

Machine learning and deep learning are subfields of AI As a whole, artificial intelligence contains many subfields, including: Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude. A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data. Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition. Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings. Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks. While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly." Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. The type of machine learning from our previous example, called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous datasets fed. Unsupervised learning, another type of machine learning, is the family of machine learning algorithms, which have main uses in pattern detection and descriptive modeling. These algorithms do not have output categories or labels on the data (the model trains with unlabeled data). What Is Deep Learning? Some consider deep learning to be the next frontier of machine learning, the cutting edge of the cutting edge. You may already have experienced the results of an in-depth deep learning program without even realizing it! If you’ve ever watched Netflix, you’ve probably seen its recommendations for what to watch. And some streaming-music services choose songs based on what you’ve listened to in the past or songs you’ve given the thumbs-up to or hit the “like” button for. Both of those capabilities are based on deep learning. Google’s voice recognition and image recognition algorithms also use deep learning. Just as machine learning is considered a type of AI, deep learning is often considered to be a type of machine learning—some call it a subset. While machine learning uses simpler concepts like predictive models, deep learning uses artificial neural networks designed to imitate the way humans think and learn. You may remember from high school biology that the primary cellular component and the main computational element of the human brain is the neuron and that each neural connection is like a small computer. The network of neurons in the brain is responsible for processing all kinds of input: visual, sensory, and so on. With deep learning computer systems, as with machine learning, the input is still fed into them, but the info is often in the form of huge data sets because deep learning systems need a large amount of data to understand it and return accurate results. Then the artificial neural networks ask a series of binary true/false questions based on the data, involving highly complex mathematical calculations, and classify that data based on the answers received. So although both machine and deep learning fall under the general classification of artificial intelligence, and both “learn” from data input, there are some key differences between the two. If you’d like to learn more specifically about deep learning, by the way, you can check out this Introduction to Deep Learning tutorial. It’s also worth learning separately about deep learning with TensorFlow, as TensorFlow is one of the most popular libraries for implementing deep learning.

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning Scikit-Learn merupakan salah satu library machine learning berbasis Python yang sangat direkomendasikan. Scikit-Learn memenuhi kebutuhan matematika, pengolahan data hingga banyak algoritma ML seperti Regresi, Classification, dan SVM. Jadi jika kalian baru saja memulai belajar AI atau Machine Learning, library ini menjadi pilihan yang tepat untuk dipelajari. Ikuti webinar trial DILo Medan x BISA AI yang akan membahas dari awal penggunaan Scikit-Learn dan beberapa kasusnya secara langsung, bersama: - M. Octaviano Pratama, S.Kom., M.Kom Chief Scientist BISA AI Acara ini akan dilaksanakan pada: Sabtu, 19 Desember 2020 14.00 WIB – Selesai Live dari BISA Tampil dan YouTube BISA AI Daftar acaranya di: bit.ly/dmonline28 --- 📌 Playlist video event workshop, seminar, dan talkshow dari berbagai narasumber mengenai Python, AI, dll di: https://www.youtube.com/playlist?list=PLwEzEX1KA1ngauZlV1lQLk7XSBtifdSgL --- 📌 Lihat event-event selanjutnya di: https://bisa.ai/tampil/event --- Visit our website at : https://bisa.ai Blog at : https://medium.com/bisa-ai Instagram : https://www.instagram.com/bisa.ai/ Contact Person (BISA.AI) : +62-8211-6654-087 for latest info and events

Adversarial machine learning tutorial Ep. 1 เสกหมีแพนด้าให้กลายเป็นแมลงสาบ | โดย AibyNeto

Adversarial machine learning tutorial Ep. 1 เสกหมีแพนด้าให้กลายเป็นแมลงสาบ ช่องทางการติดตาม Neto-san Facebook: AI powered by Neto-san YouTube: AibyNeto #Neuralnetwork #Deeplearning #Machinelearning #Adversarialexamples

Can An AI Design Our Tax Policy? 📊

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies" is available here: https://ift.tt/2KJTKdv 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Azure AI Batch 2: No-Code Machine Learning and Natural Language Processing

KDD 2020: Hands-On Tutorials: From Zero to AI Hero with Automated Machine Learning

Aniththa Umamahesan: Microsoft; Deepak Mukunthu Iyappan Babu: Microsoft; Cesar De la Torre Lorente: Microsoft; Anup Shirgaonkar: Microsoft

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning Scikit-Learn merupakan salah satu library machine learning berbasis Python yang sangat direkomendasikan. Scikit-Learn memenuhi kebutuhan matematika, pengolahan data hingga banyak algoritma ML seperti Regresi, Classification, dan SVM. Jadi jika kalian baru saja memulai belajar AI atau Machine Learning, library ini menjadi pilihan yang tepat untuk dipelajari. Ikuti webinar trial DILo Medan x BISA AI yang akan membahas dari awal penggunaan Scikit-Learn dan beberapa kasusnya secara langsung, bersama: - M. Octaviano Pratama, S.Kom., M.Kom Chief Scientist BISA AI Acara ini akan dilaksanakan pada: Sabtu, 19 Desember 2020 14.00 WIB – Selesai Live dari BISA Tampil dan YouTube BISA AI Daftar acaranya di: bit.ly/dmonline28 --- 📌 Playlist video event workshop, seminar, dan talkshow dari berbagai narasumber mengenai Python, AI, dll di: https://www.youtube.com/playlist?list=PLwEzEX1KA1ngauZlV1lQLk7XSBtifdSgL --- 📌 Lihat event-event selanjutnya di: https://bisa.ai/tampil/event --- Visit our website at : https://bisa.ai Blog at : https://medium.com/bisa-ai Instagram : https://www.instagram.com/bisa.ai/ Contact Person (BISA.AI) : +62-8211-6654-087 for latest info and events

Webinar Indonesia ID5G Ecosystem x BISA AI #35 – Tutorial Apache Airflow untuk ETL pada Big Data, Bu

Webinar Indonesia ID5G Ecosystem x BISA AI #35 – Tutorial Apache Airflow untuk ETL pada Big Data, Business Intelligence, dan Machine Learning Pada bidang Big Data, Business Intelligence, dan Machine Learning ada banyak data yang saling berpindah dari satu tempat ke tempat lain dalam berbagai bentuk. Data-data tersebut dalam skala besar tidak hanya berpindah begitu saja, harus ada alur atau sistem yang menangkap dan menjelaskan alur data tersebut. Disinilah Apache Airflow digunakan sebagai framework untuk urusan data workflow tersebut. Ikuti webinar ID5G x BISA AI #35 untuk belajar bersama-sama bagaimana data berpindah dan manfaatnya pada sistem, bersama : - M. Octaviano Pratama, S.Kom., M.Kom Co-Founder BISA AI Acara ini akan dilaksanakan pada : Jum'at, 18 Desember 2020 19.00 WIB – Selesai Langsung dari YouTube BISA AI dan BISA Tampil --- 📌 Playlist video event workshop, seminar, dan talkshow dari berbagai narasumber mengenai Python, AI, dll di: https://www.youtube.com/playlist?list=PLwEzEX1KA1ngauZlV1lQLk7XSBtifdSgL --- 📌 Lihat event-event selanjutnya di: https://bisa.ai/tampil/event --- Visit our website at : https://bisa.ai Blog at : https://medium.com/bisa-ai Instagram : https://www.instagram.com/bisa.ai/ Contact Person (BISA.AI) : +62-8211-6654-087 for latest info and events

NeurIPS 2020 | State of the art in Explaining Machine Learning Predictions (Tutorial)

Join this channel to get access to perks ⇢ https://www.youtube.com/c/AIPursuit/join Subscribe ⇢ https://www.youtube.com/c/AIPursuit?sub_confirmation=1 Paypal ⇢ https://paypal.me/tayhengee Patreon ⇢ https://patreon.com/aipursuit The video is reposted for educational purposes and encourages involvement in the field of research.

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning

Webinar Trial DILo Medan x BISA AI – Tutorial Scikit Learn Untuk Machine Learning Scikit-Learn merupakan salah satu library machine learning berbasis Python yang sangat direkomendasikan. Scikit-Learn memenuhi kebutuhan matematika, pengolahan data hingga banyak algoritma ML seperti Regresi, Classification, dan SVM. Jadi jika kalian baru saja memulai belajar AI atau Machine Learning, library ini menjadi pilihan yang tepat untuk dipelajari. Ikuti webinar trial DILo Medan x BISA AI yang akan membahas dari awal penggunaan Scikit-Learn dan beberapa kasusnya secara langsung, bersama: - M. Octaviano Pratama, S.Kom., M.Kom Chief Scientist BISA AI Acara ini akan dilaksanakan pada: Sabtu, 19 Desember 2020 14.00 WIB – Selesai Live dari BISA Tampil dan YouTube BISA AI Daftar acaranya di: bit.ly/dmonline28 --- 📌 Playlist video event workshop, seminar, dan talkshow dari berbagai narasumber mengenai Python, AI, dll di: https://www.youtube.com/playlist?list=PLwEzEX1KA1ngauZlV1lQLk7XSBtifdSgL --- 📌 Lihat event-event selanjutnya di: https://bisa.ai/tampil/event --- Visit our website at : https://bisa.ai Blog at : https://medium.com/bisa-ai Instagram : https://www.instagram.com/bisa.ai/ Contact Person (BISA.AI) : +62-8211-6654-087 for latest info and events

ReBeL - Combining Deep Reinforcement Learning and Search for Imperfect-Information Games (Explained)

#ai #technology #poker This paper does for Poker what AlphaZero has done for Chess & Go. The combination of Self-Play Reinforcement Learning and Tree Search has had tremendous success in perfect-information games, but transferring such techniques to imperfect information games is a hard problem. Not only does ReBeL solve this problem, but it provably converges to a Nash Equilibrium and delivers a superhuman Heads Up No-Limit Hold'em bot with very little domain knowledge. OUTLINE: 0:00 - Intro & Overview 3:20 - Rock, Paper, and Double Scissor 10:00 - AlphaZero Tree Search 18:30 - Notation Setup: Infostates & Nash Equilibria 31:45 - One Card Poker: Introducing Belief Representations 45:00 - Solving Games in Belief Representation 55:20 - The ReBeL Algorithm 1:04:00 - Theory & Experiment Results 1:07:00 - Broader Impact 1:10:20 - High-Level Summary Paper: https://ift.tt/2P73X5O Code: https://ift.tt/2BocTR8 Blog: https://ift.tt/36AgHLW ERRATA: As someone last video pointed out: This is not the best Poker algorithm, but the best one that uses very little expert knowledge. Abstract: The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of successes in single-agent settings and perfect-information games, best exemplified by AlphaZero. However, prior algorithms of this form cannot cope with imperfect-information games. This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player zero-sum game. In the simpler setting of perfect-information games, ReBeL reduces to an algorithm similar to AlphaZero. Results in two different imperfect-information games show ReBeL converges to an approximate Nash equilibrium. We also show ReBeL achieves superhuman performance in heads-up no-limit Texas hold'em poker, while using far less domain knowledge than any prior poker AI. Authors: Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Webinar: Recent Trends in Artificial Intelligence and Machine Learning

Scroobly: Motion capture for character animation - Made with TensorFlow.js

On this episode of Made with TensorFlow.js we’re joined by Shan Huang from China, who’s built upon her previous Pose Animator project to make Scroobly, a fun app which brings doodles to life in real-time using your camera. Scroobly uses Facemesh and PoseNet to map your live motion and updates the animation as you move! Hosted by Jason Mayes, Senior Developer Advocate for TensorFlow. Scroobly → https://goo.gle/379dK5x Pose Animator → https://ift.tt/2ZJDTo2 Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

NeurIPS 2020 | State of the art in Explaining Machine Learning Predictions (Tutorial)

Join this channel to get access to perks ⇢ https://www.youtube.com/c/AIPursuit/join Subscribe ⇢ https://www.youtube.com/c/AIPursuit?sub_confirmation=1 Paypal ⇢ https://paypal.me/tayhengee Patreon ⇢ https://patreon.com/aipursuit The video is reposted for educational purposes and encourages involvement in the field of research.

What Is This 3D Photography Thing? 🎑

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/2WfaQph 📝 The paper "One Shot 3D Photography" is available here: https://ift.tt/3lto9xZ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Automated Depth Maps - A.I. Machine Learning

Learn more: https://runwayml.com/

handwritten character recognition using deep learning #dl #ml #ai

AI And Machine Learning: The Impact, Limits, And Potential Of AI

2M All-In into $5 Pot! WWYD? Daniel Negreanu's No-Limit Hold'em Challenge! (Poker Hand Analysis)

Daniel Negreanu posted a set of very interesting No-Limit Hold'em situations on Twitter. I try to analyze them from the perspective of a poker bot. See how such bots think about the game and approximate Nash equilibria. https://twitter.com/RealKidPoker/status/1337887509397741568 https://twitter.com/RealKidPoker/status/1337899147337244673 https://twitter.com/RealKidPoker/status/1337904860721606656 Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV BiliBili: https://ift.tt/3mfyjkW Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

AI & Machine Learning Workshop: The Tutorial before your Tutorial - Part 1

Artificial Intelligence and Machine Learning with TensorFlow/Keras is a confusing and sometimes incomprehensible subject to learn on your own. The Google Machine Learning Crash Course is a good tutorial to learn AI/ML if you already have a background on the subject. The purpose of this workshop is the be the tutorial before to take the Google tutorial. I've been there and now I'm ready to pass it forward and share what I've learned. I'm not an expert but I have working code examples that I will use to teach you based on my current level of understanding of the subject.

TensorFlow.js Community "Show & Tell" #4

6 new demos from the #MadeWithTFJS global community pushing the boundaries of on device machine learning in JavaScript. Hosted by Jason Mayes. TensorFlow.js Show & Tell music video → https://goo.gle/3qR2NNQ Scroobly → https://goo.gle/379dK5x Soduku in-depth how it was made → https://goo.gle/2W9fkxC Soduku live demo → https://goo.gle/3qNGx7m AVA - Automated Videoing Assistant → https://goo.gle/3m4dAQY AVA Twitter → https://goo.gle/3n5xA74 AR face filters demo → https://goo.gle/2W7MX3c AR face filters GitHub → https://goo.gle/3a0yH4a Andreas Schallwig GitHub → https://goo.gle/3me7cXo Mona Lisa Effect → https://goo.gle/376SehI Mona Lisa blog post → https://goo.gle/343ESku Watch past #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Catch more of the TensorFlow.js Community Show & Tell series → http://goo.gle/tf-show-and-tell Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

Soft Body Wiggles And Jiggles…Effortlessly! 🐘

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/39vhPCn 📝 The paper "Complementary Dynamics" is available here: https://ift.tt/3a8glyx 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

AI & Machine Learning Workshop: The Tutorial before your Tutorial - Part 1

Artificial Intelligence and Machine Learning with TensorFlow/Keras is a confusing and sometimes incomprehensible subject to learn on your own. The Google Machine Learning Crash Course is a good tutorial to learn AI/ML if you already have a background on the subject. The purpose of this workshop is the be the tutorial before to take the Google tutorial. I've been there and now I'm ready to pass it forward and share what I've learned. I'm not an expert but I have working code examples that I will use to teach you based on my current level of understanding of the subject.

AI Final Project

Final Project For DSCI 6670 Artificial intelligence Cancer Diagnosis Using Machine Learning

Student Information ChatBot (AI Machine Learning)

Simulating Honey And Hot Showers For Bunnies! 🍯🐰

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/2K6NoaV 📝 The paper "An Adaptive Variational Finite Difference Framework for Efficient Symmetric Octree Viscosity" is available here: https://ift.tt/39UL4Ps Houdini video: https://ift.tt/3qHlfYT 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Thumbnail background image credit: https://ift.tt/3mZ98nY Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Python for Deep Learning | Deep Learning Tutorial For Beginners | Edureka Live

🔥Edureka Tensorflow Training - https://www.edureka.co/ai-deep-learni... This video on "Python for Deep Learning" will provide you with detailed and comprehensive knowledge of Deep Learning, How it came into emergence. The various subparts of Data Science, how they are related, and How Deep Learning is revolutionalizing the world we live in. 🔷🔸𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗦𝗽𝗲𝗮𝗸𝗲𝗿🔸🔷 Name: Mr. Sandeep Sharma Details: 1. Former Vice-President - Products at Eye Care Leaders. 2. Led Data Science, Product, and Engineering teams of 150+ globally. 3. 20+ years of global experience in Consulting, Products, and Development. 4. An Alumnus of IIT Bombay. 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV ------------------------------------Edureka Online Training and Certification--------------------------------- 🔵 DevOps Online Training: https://bit.ly/2BPwXf0 🟣 Python Online Training: https://bit.ly/2CQYGN7 🔵 AWS Online Training: https://bit.ly/2ZnbW3s 🟣 RPA Online Training: https://bit.ly/2Zd0ac0 🔵 Data Science Online Training: https://bit.ly/2NCT239 🟣 Big Data Online Training: https://bit.ly/3g8zksu 🔵 Java Online Training: https://bit.ly/31rxJcY 🟣 Selenium Online Training: https://bit.ly/3dIrh43 🔵 PMP Online Training: https://bit.ly/3dJxMTW 🟣 Tableau Online Training: https://bit.ly/3g784KJ -----------------------------------------Edureka Masters Programs--------------------------------------------------- 🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp 🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ 🔵Data Scientist Masters Program: https://bit.ly/2YHaolS 🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv 🔵Machine Learning Engineer Masters Program: https://bit.ly/388NXJi 🟣Business Intelligence Masters Program: https://bit.ly/2BPLtn2 🔵Python Developer Masters Program: https://bit.ly/2Vn7tgb 🟣RPA Developer Masters Program: https://bit.ly/3eHwPNf -----------------------------------------Edureka PGP Courses--------------------------------------------------- 🔵Artificial and Machine Learning PGP: https://bit.ly/2Ziy7b1 🟣CyberSecurity PGP: https://bit.ly/3eHvI0h 🔵Digital Marketing PGP: https://bit.ly/38cqdnz 🟣Big Data Engineering PGP: https://bit.ly/3eTSyBC 🔵Data Science PGP: https://bit.ly/3dIeYV9 🟣Cloud Computing PGP: https://bit.ly/2B9tHLP ----------------------------------------------------------------- Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_lea... Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?count... Meetup: https://www.meetup.com/edureka/ #edureka #DeepLearningEdureka #DeepLearningwithPython #DeepLearning #Pythontutorial #Pythononlinetraining #Pythonforbeginners --------------------------------------------------------------- How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies - - - - - - - - - - - - - - For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

How to pass AWS Machine Learning Specialty Exam #machinelearning #aws #certification

#ai #machinelearning #aws #awscertification #ml #simpledataflow This is the tutorial on how to pass AWS Machine Learning Specialty Exam - Text tutorial: https://simpledataflow.com/machine-learning-specialty-exam/

These Are Pixels Made of Wood! 🌲🧩

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Computational Parquetry: Fabricated Style Transfer with Wood Pixels" is available here: https://ift.tt/3gcNmdC ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

ML engineering for production ML deployments with TFX (TensorFlow Fall 2020 Updates)

Delivering the results of advanced ML technology to customers requires a rigorous approach and production-ready systems. In this quick overview learn about TFX, the framework that Google uses to put ML into production. In this session Developer Advocate Robert Crowe (@robert_crowe) covers the basics, and highlights what's new this year, to help you get started. We'll also show you a hands-on look at how to put together a production pipeline system with TFX. We'll quickly cover everything from data acquisition, model building, through to deployment and management. Learn more about TFX → https://goo.gle/2YymOcC A brief history of TFX → https://goo.gle/3pFOvPc Repo → https://goo.gle/337fW9J Community → https://goo.gle/2OGjw78 TFX on YouTube → https://goo.gle/2xVkwt4 Check out more TF Fall 2020 Updates → https://goo.gle/tf-fall-updates Subscribe to TensorFlow → https://goo.gle/TensorFlow #tensorflowupdates

This Robot Learned To Climb Any Terrain! 🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their mentioned post is available here: https://ift.tt/39vhPCn 📝 The paper "Learning Quadrupedal Locomotion over Challenging Terrain " is available here: https://ift.tt/35t3cff 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Superpowers with TensorFlow.js (TF Fall 2020 Updates)

Discover how to achieve superpowers in the browser and beyond by embracing machine learning in JavaScript using TensorFlow.js in this speedy 30 minute talk by Jason Mayes, Senior Developer Advocate for TensorFlow (@jason_mayes). Get inspired through a whole bunch of creative prototypes and then take your own first steps with machine learning in minutes. By the end of the talk everyone will understand how to do computer vision in the browser which you can use in any creative way you can imagine. Familiarity with JavaScript is advantageous. Come take your first steps with TensorFlow.js! Learn more about TensorFlow.js → https://goo.gle/2XLhMe0 Pretraited TF.js models → https://goo.gle/3lOlVcc Object detection demo → https://goo.gle/3pE2o0p Facemesh demo → https://goo.gle/39uDFDr Teachable Machine → https://goo.gle/3kELKtL Watch more of Made with TensorFlow.js → http://goo.gle/made-with-tfjs Check out more TF Fall 2020 Updates → https://goo.gle/tf-fall-updates Subscribe to TensorFlow → https://goo.gle/TensorFlow #tensorflowupdates

DeepMind's AlphaFold 2 Explained! AI Breakthrough in Protein Folding! What we know (& what we don't)

#deepmind #biology #ai This is Biology's AlexNet moment! DeepMind solves a 50-year old problem in Protein Folding Prediction. AlphaFold 2 improves over DeepMind's 2018 AlphaFold system with a new architecture and massively outperforms all competition. In this Video, we take a look at how AlphaFold 1 works and what we can gather about AlphaFold 2 from the little information that's out there. OUTLINE: 0:00 - Intro & Overview 3:10 - Proteins & Protein Folding 14:20 - AlphaFold 1 Overview 18:20 - Optimizing a differentiable geometric model at inference 25:40 - Learning the Spatial Graph Distance Matrix 31:20 - Multiple Sequence Alignment of Evolutionarily Similar Sequences 39:40 - Distance Matrix Output Results 43:45 - Guessing AlphaFold 2 (it's Transformers) 53:30 - Conclusion & Comments AlphaFold 2 Blog: https://ift.tt/2Vl73qd AlphaFold 1 Blog: https://ift.tt/2uMRsWg AlphaFold 1 Paper: https://ift.tt/2QVIr61 MSA Reference: https://ift.tt/2VnXaYO CASP14 Challenge: https://ift.tt/2L0CKTX Paper Title: High Accuracy Protein Structure Prediction Using Deep Learning Abstract: Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into is known as the “protein folding problem”, and has stood as a grand challenge in biology for the past 50 years. In a major scientific advance, the latest version of our AI system AlphaFold has been recognised as a solution to this grand challenge by the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP). This breakthrough demonstrates the impact AI can have on scientific discovery and its potential to dramatically accelerate progress in some of the most fundamental fields that explain and shape our world. Authors: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Kathryn Tunyasuvunakool, Olaf Ronneberger, Russ Bates, Augustin Žídek, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Martin Steinegger, Michalina Pacholska, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis. Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

AI Generates New Classical Music - Machine Learning using LSTM

Codes in The Description

Predictive Coding Approximates Backprop along Arbitrary Computation Graphs (Paper Explained)

#ai #biology #neuroscience Backpropagation is the workhorse of modern deep learning and a core component of most frameworks, but it has long been known that it is not biologically plausible, driving a divide between neuroscience and machine learning. This paper shows that Predictive Coding, a much more biologically plausible algorithm, can approximate Backpropagation for any computation graph, which they verify experimentally by building and training CNNs and LSTMs using Predictive Coding. This suggests that the brain and deep neural networks could be much more similar than previously believed. OUTLINE: 0:00 - Intro & Overview 3:00 - Backpropagation & Biology 7:40 - Experimental Results 8:40 - Predictive Coding 29:00 - Pseudocode 32:10 - Predictive Coding approximates Backprop 35:00 - Hebbian Updates 36:35 - Code Walkthrough 46:30 - Conclusion & Comments Paper: https://ift.tt/3mndCnZ Code: https://ift.tt/35bLGvP Abstract: Backpropagation of error (backprop) is a powerful algorithm for training machine learning architectures through end-to-end differentiation. However, backprop is often criticised for lacking biological plausibility. Recently, it has been shown that backprop in multilayer-perceptrons (MLPs) can be approximated using predictive coding, a biologically-plausible process theory of cortical computation which relies only on local and Hebbian updates. The power of backprop, however, lies not in its instantiation in MLPs, but rather in the concept of automatic differentiation which allows for the optimisation of any differentiable program expressed as a computation graph. Here, we demonstrate that predictive coding converges asymptotically (and in practice rapidly) to exact backprop gradients on arbitrary computation graphs using only local learning rules. We apply this result to develop a straightforward strategy to translate core machine learning architectures into their predictive coding equivalents. We construct predictive coding CNNs, RNNs, and the more complex LSTMs, which include a non-layer-like branching internal graph structure and multiplicative interactions. Our models perform equivalently to backprop on challenging machine learning benchmarks, while utilising only local and (mostly) Hebbian plasticity. Our method raises the potential that standard machine learning algorithms could in principle be directly implemented in neural circuitry, and may also contribute to the development of completely distributed neuromorphic architectures. Authors: Beren Millidge, Alexander Tschantz, Christopher L. Buckley Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Webinar Indonesia ID5G Ecosystem x BISA AI #38 – Tutorial Orange Untuk Data Science dan Machine Lear

Webinar Indonesia ID5G Ecosystem x BISA AI #38 – Tutorial Orange Untuk Data Science dan Machine Learning Orange adalah software analisis dan visualisasi data tanpa kode. Untuk pemula, software ini cocok digunakan sebagai pengenalan dan eksplorasi dasar-dasar Data Science dan Machine Learning. Namun ada juga fitur-fitur yang dapat digunakan untuk membantu modelling dan projek tingkat lanjut lainnya. Ikuti webinar ID5G x BISA AI #38 untuk sama-sama belajar Data Science dan Machine Learning tanpa menggunakan kodingan! Bersama : - M. Octaviano Pratama, S.Kom., M.Kom Co Founder BISA AI Acara ini akan dilaksanakan pada : Kamis, 03 Desember 2020 19.00 WIB – Selesai Langsung dari BISA Tampil dan YouTube BISA AI --- 📌 Playlist video event workshop, seminar, dan talkshow dari berbagai narasumber mengenai Python, AI, dll di: https://www.youtube.com/playlist?list=PLwEzEX1KA1ngauZlV1lQLk7XSBtifdSgL --- 📌 Lihat event-event selanjutnya di: https://bisa.ai/tampil/event --- Visit our website at : https://bisa.ai Blog at : https://medium.com/bisa-ai Instagram : https://www.instagram.com/bisa.ai/ Contact Person (BISA.AI) : +62-8211-6654-087 for latest info and events

Tutorial on Artificial Intelligence: Deep Learning: Methodology Season #2/1.

Check out the Season 2 of our 12-episode web series dedicated to AI This 1st episode features Alexandre Valentian who walks us through the requirements for DeepLearning methodology, including the need to clearly define the problem and prepare & collect data ....So we hope this teaser makes you want to view and enjoy the first cap of Season#2 on Artificial Intelligence ! For more information ⏩Visit CEA-List Website: http://www-list.cea.fr/en/ ⏩Follow them on LinkedIn: linkedin.com/company/cealist ⏩Visit MIAI website: https://miai.univ-grenoble-alpes.fr/ ⏩Follow them on LinkedIn: linkedin.com/company/miai-grenoble-alpes/ ⏩ Visit our Website: https://www.leti-cea.com/cea-tech/leti... ⏩ Follow us on LinkedIn: linkedin.com/company/leti ⏩ Don't miss our corporate video : http://bit.ly/CEALeti 🔔 Subscribe to our channel: http://bit.ly/suscribe-CEALeti Thank you for watching !

Remember, This Meeting Never Happened! 🚶🚶‍♀️

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their report on this exact paper is available here: https://ift.tt/37iZZjd 📝 The paper "Layered Neural Rendering for Retiming People in Video" is available here: https://ift.tt/3cGOmoP 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Webinar Indonesia ID5G Ecosystem x BISA AI #38 – Tutorial Orange Untuk Data Science dan Machine Lear

Webinar Indonesia ID5G Ecosystem x BISA AI #38 – Tutorial Orange Untuk Data Science dan Machine Learning Orange adalah software analisis dan visualisasi data tanpa kode. Untuk pemula, software ini cocok digunakan sebagai pengenalan dan eksplorasi dasar-dasar Data Science dan Machine Learning. Namun ada juga fitur-fitur yang dapat digunakan untuk membantu modelling dan projek tingkat lanjut lainnya. Ikuti webinar ID5G x BISA AI #38 untuk sama-sama belajar Data Science dan Machine Learning tanpa menggunakan kodingan! Bersama : - M. Octaviano Pratama, S.Kom., M.Kom Co Founder BISA AI Acara ini akan dilaksanakan pada : Kamis, 03 Desember 2020 19.00 WIB – Selesai Langsung dari BISA Tampil dan YouTube BISA AI --- 📌 Playlist video event workshop, seminar, dan talkshow dari berbagai narasumber mengenai Python, AI, dll di: https://www.youtube.com/playlist?list=PLwEzEX1KA1ngauZlV1lQLk7XSBtifdSgL --- 📌 Lihat event-event selanjutnya di: https://bisa.ai/tampil/event --- Visit our website at : https://bisa.ai Blog at : https://medium.com/bisa-ai Instagram : https://www.instagram.com/bisa.ai/ Contact Person (BISA.AI) : +62-8211-6654-087 for latest info and events

Tutorial on Artificial Intelligence: Deep Learning: Methodology Season #2/1.

Check out the Season 2 of our 12-episode web series dedicated to AI This 1st episode features Alexandre Valentian who walks us through the requirements for DeepLearning methodology, including the need to clearly define the problem and prepare & collect data ....So we hope this teaser makes you want to view and enjoy the first cap of Season#2 on Artificial Intelligence ! For more information ⏩Visit CEA-List Website: http://www-list.cea.fr/en/ ⏩Follow them on LinkedIn: linkedin.com/company/cealist ⏩Visit MIAI website: https://miai.univ-grenoble-alpes.fr/ ⏩Follow them on LinkedIn: linkedin.com/company/miai-grenoble-alpes/ ⏩ Visit our Website: https://www.leti-cea.com/cea-tech/leti... ⏩ Follow us on LinkedIn: linkedin.com/company/leti ⏩ Don't miss our corporate video : http://bit.ly/CEALeti 🔔 Subscribe to our channel: http://bit.ly/suscribe-CEALeti Thank you for watching !

Univ.AI | Demo class 2 | The Essence of Machine Learning by Dr. Rahul Dave

Training and deploying ML models on edge devices (TF Fall 2020 Updates)

Learn how to train and deploy an ML model on an Android app in just a few lines of code with TensorFlow Lite Model Maker and Android Studio. From here you can then explore how to use various tools from Google to turn a prototype into a production app. Presented by Khanh LeViet, Developer Advocate for TensorFlow (@khanlvg). Flower classification codelab → https://goo.gle/3pJeh56 TF Lite pretrained models → https://goo.gle/2Uvr3a9 Learn more about TF Lite → https://goo.gle/2Wk5MPM Check out more TF Fall 2020 updates → https://goo.gle/tf-fall-updates Subscribe to TensorFlow → https://goo.gle/TensorFlow #tensorflowupdates

AI-Based Sky Replacement Is Here! 🌓

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2YuG7Yf ❤️ Their report on this paper is available here: https://ift.tt/2IVQf6H 📝 The paper "Castle in the Sky: Dynamic Sky Replacement and Harmonization in Videos" is available here: https://ift.tt/2TpVflx ☀️The mentioned free light transport course is available here: https://ift.tt/2rdtvDu 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Univ.AI | Demo class 2 | The Essence of Machine Learning by Dr. Rahul Dave

Intro to On-device Machine Learning (TF Fall 2020 Updates)

In this session, Developer Advocate Khanh LeViet (@khanhlvg) talks about TensorFlow Lite, the framework that brings ML to mobile and embedded systems. You'll learn about the differences between ML on a supercomputer and ML on a portable device, and the tools and technologies that Google has developed to allow you to bring your work to mobile devices without reinventing the wheel. We'll cover the basics and also special subjects like TinyML and model optimization and quantization. Learn more about TensorFlow Lite → https://goo.gle/2Wk5MPM TensorFlow Lite example apps → https://goo.gle/3byxWNf TensorFlow Lite for Microcontrollers → https://goo.gle/2yiYyUl Robotic vacuum cleaner powered by TF → https://goo.gle/36MmhtI Check out more TF Fall 2020 updates → https://goo.gle/tf-fall-updates Subscribe to TensorFlow → https://goo.gle/TensorFlow

Fourier Neural Operator for Parametric Partial Differential Equations (Paper Explained)

#ai #research #engineering Numerical solvers for Partial Differential Equations are notoriously slow. They need to evolve their state by tiny steps in order to stay accurate, and they need to repeat this for each new problem. Neural Fourier Operators, the architecture proposed in this paper, can evolve a PDE in time by a single forward pass, and do so for an entire family of PDEs, as long as the training set covers them well. By performing crucial operations only in Fourier Space, this new architecture is also independent of the discretization or sampling of the underlying signal and has the potential to speed up many scientific applications. OUTLINE: 0:00 - Intro & Overview 6:15 - Navier Stokes Problem Statement 11:00 - Formal Problem Definition 15:00 - Neural Operator 31:30 - Fourier Neural Operator 48:15 - Experimental Examples 50:35 - Code Walkthrough 1:01:00 - Summary & Conclusion Paper: https://ift.tt/2UQQguM Blog: https://ift.tt/3kUGMZW Code: https://ift.tt/3nMENJf MIT Technology Review: https://ift.tt/31XXdy7 Abstract: The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation. In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers' equation, Darcy flow, and the Navier-Stokes equation (including the turbulent regime). Our Fourier neural operator shows state-of-the-art performance compared to existing neural network methodologies and it is up to three orders of magnitude faster compared to traditional PDE solvers. Authors: Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Near-Perfect Virtual Hands For Virtual Reality! 👐

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "MEgATrack: Monochrome Egocentric Articulated Hand-Tracking for Virtual Reality" is available here: https://ift.tt/3lRFHn3 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. If you wish to support the series, click here: https://ift.tt/2icTBUb Thumbnail background image credit: https://ift.tt/335bVUI Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m