Busting Failed Simulations Since 2021! 👕

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Fast Linking Numbers for Topology Verification of Loopy Structures " is available here: https://ift.tt/2VcjFmV ❤️ 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, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Federated learning: Basics and application to the mobile keyboard (ML Tech Talks)

In this session of Machine Learning Tech Talks, Research Scientist Françoise Beaufays will discuss the basics and application of the mobile keyboard for federated learning. Chapters: 0:00 - Introduction 0:43 - Intro to federated learning 4:20 - Mobile keyboards 8:32 - Making federated learning work in practice 23:09 - Applying federated learning to mobile keyboards 31:05 - Where to learn more Resource: Federated Learning → http://g.co/federated Catch more ML Tech Talks → http://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow

Leveraging context features and multitask learning (Building recommendation systems with TensorFlow)

In this video, we are going to learn how to leverage context features to improve the accuracy of your recommendation models and multitask learning to optimize multiple metrics of your system. Leveraging context features → https://goo.gle/3kZJmSc Building a multitask recommender using TensorFlow Recommenders → https://goo.gle/3iPfnd2 Watch more Building recommendation systems with TensorFlow → https://goo.gle/3Bi8NUS Subscribe to TensorFlow → https://goo.gle/TensorFlow

Robotics and AI Learning Program || Day- 13 session || Bharat AI Labs

Create an Analog and Digital Clock In Scratch using Block Coding

Data Visualization Using Tableau : Part 2 | Tutorial for Beginners | Tableau Training - Free Live

Why learn Tableau? #Tableau is a leader in Gartner's magic quadrant in the #BI & #Dataanalytic field. Being used in various leading domains like #BusinessIntelligence, #Marketing, #BigData #Hadoop, etc., Tableau is one skill set most professionals wish to carry ahead in their careers. As per Indeed.com, the mean salary of a Tableau professional is $ 100,000. Tableau is a great way to learn about #datavisualisation. Data is only as good as how it is interpreted, and to get the most out of the data we have, it must be well-analyzed and presented. Tableau and #PowerBI are business analytics and data visualization solutions that emphasize interactive business intelligence and visualization functions and a user-friendly interface for creating reports and dashboards. Tableau aims to transform data sources into actionable, interactive, and visually compelling reports and insights. #TableauFullCourse #TableauTraining #TableauTutorial #TableauTutorialForBeginners #TableauDashboard #TableauTraining #TableauCertification #DataVisualizationusingTableau #HowtovisualizedatainTableau #TableauDataVisualization #TableauTraining #DataVisualization #TableauCourse#tableauCharts #tableauTraining #tableauTutorial #tableauCertification #tableauDataanalysis #tableauvisualization #tableau Do you wish to learn more? Are you looking for something more? Enroll in Learnbay Data Science Certification Course. This #DataScience with Python course will teach you how to use Python to grasp data science and analytics approaches. You'll study the fundamentals of #Pythonprogramming and become an expert in data analytics, #machinelearning, data visualization, web scraping, and natural language processing with our Python for Data Science Course. Many data science jobs require Python, so get a head start on your career with this interactive, hands-on training. Subscribe to our channel for concepts relating to machine learning : https://www.youtube.com/channel/UC-nt... To learn in detail about Data Visualization, data visualization using tableau, charts, graphs, etc visit: https://bit.ly/3x6dbmS Watch detailed videos on : Python for data science: https://www.youtube.com/playlist?list=PLl1gyDCKkiQQ8Z-d5w4a22JKuKRPdjqNe Data science full course structure and syllabus: https://www.youtube.com/playlist?list=PLl1gyDCKkiQSfppJ4EPIDlvNPmPSKUQad Machine learning course: https://www.youtube.com/playlist?list=PLl1gyDCKkiQS3N0UqEtwAzG9l8l6eH_-0 Statistics for data science: https://www.youtube.com/playlist?list=PLl1gyDCKkiQRoADJw0YyvUdw9J9RSBjbJ Data visualization detailed course: https://youtu.be/cfYKC0l-ODg https://youtu.be/VRAqpvPdMHk Learn more: http://bit.ly/DatascienceCourse For more updates on courses/data science, artificial intelligence, machine learning check out : Linkedin: https://www.linkedin.com/company/10037723/admin/ Facebook: https://www.facebook.com/learnbay Instagram: https://www.instagram.com/learnbay_datascience/ Website: https://www.learnbay.co/data-science-course/data-science-and-ai/

DeepMind’s Robot Inserts A USB Stick! 🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2S5tXnb ❤️ Their mentioned post is available here: https://ift.tt/3pzoPUa 📝 The paper "Scaling data-driven robotics with reward sketching and batch reinforcement learning" is available here: https://ift.tt/3zHE4PC 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Data Visualization Using Tableau : Part 1 | Tutorial for Beginners | Tableau Training - Free Live

Why learn Tableau? #Tableau is a leader in Gartner's magic quadrant in the #BI & #Dataanalytic field. Being used in various leading domains like #BusinessIntelligence, #Marketing, #BigData #Hadoop, etc., Tableau is one skill set most professionals to wish to carry ahead in their careers. As per Indeed.com, the mean salary of a Tableau professional is $ 100,000. Tableau is a great way to learn about #datavisualisation. Data is only as good as how it is interpreted, and to get the most out of the data we have, it must be well-analyzed and presented. Tableau and #PowerBI are business analytics and data visualization solutions that emphasize interactive business intelligence and visualization functions and a user-friendly interface for creating reports and dashboards. Tableau aims to transform data sources into actionable, interactive, and visually compelling reports and insights. #TableauFullCourse #TableauTraining #TableauTutorial #TableauTutorialForBeginners #TableauDashboard #TableauTraining #TableauCertification #DataVisualizationusingTableau #HowtovisualizedatainTableau #TableauDataVisualization #TableauTraining #DataVisualization #TableauCourse#tableauCharts #tableauTraining #tableauTutorial #tableauCertification #tableauDataanalysis #tableauvisualization #tableau Do you wish to learn more? Are you looking for something more? Enroll in Learnbay Data Science Certification Course. This #DataScience with Python course will teach you how to use Python to grasp data science and analytics approaches. You'll study the fundamentals of #Pythonprogramming and become an expert in data analytics, #machinelearning, data visualization, web scraping, and natural language processing with our Python for Data Science Course. Many data science jobs require Python, so get a head start on your career with this interactive, hands-on training. Subscribe to our channel for concepts relating to machine learning : https://www.youtube.com/channel/UC-nt... To learn in detail about Data Visualization, data visualization using tableau, charts , graphs, etc visit : https://bit.ly/3x6dbmS Watch detailed videos on : Python for data science: https://www.youtube.com/playlist?list=PLl1gyDCKkiQQ8Z-d5w4a22JKuKRPdjqNe Data science full course structure and syllabus: https://www.youtube.com/playlist?list=PLl1gyDCKkiQSfppJ4EPIDlvNPmPSKUQad Machine learning course: https://www.youtube.com/playlist?list=PLl1gyDCKkiQS3N0UqEtwAzG9l8l6eH_-0 Statistics for data science: https://www.youtube.com/playlist?list=PLl1gyDCKkiQRoADJw0YyvUdw9J9RSBjbJ Data visualization detailed course: https://youtu.be/cfYKC0l-ODg https://youtu.be/VRAqpvPdMHk Learn more: http://bit.ly/DatascienceCourse For more updates on courses/data science, artificial intelligence, machine learning check out : Linkedin: https://www.linkedin.com/company/10037723/admin/ Facebook : https://www.facebook.com/learnbay Instagram : https://www.instagram.com/learnbay_datascience/ Website : https://www.learnbay.co/data-science-course/data-science-and-ai/

Data Visualization Using Tableau : Part 2 | Tutorial for Beginners | Tableau Training - Free Live

Why learn Tableau? #Tableau is a leader in Gartner's magic quadrant in the #BI & #Dataanalytic field. Being used in various leading domains like #BusinessIntelligence, #Marketing, #BigData #Hadoop, etc., Tableau is one skill set most professionals wish to carry ahead in their career. As per Indeed.com, the mean salary of a Tableau professional is $ 100,000. Tableau is a great way to learn about #datavisualisation. Data is only as good as how it is interpreted, and to get the most out of the data we have, it must be well-analyzed and presented. Tableau and #PowerBI are business analytics and data visualisation solutions that emphasise interactive business intelligence and visualisation functions and a user-friendly interface for creating reports and dashboards. Tableau aims to transform data sources into actionable, interactive, and visually compelling reports and insights. #TableauFullCourse #TableauTraining #TableauTutorial #TableauTutorialForBeginners #TableauDashboard #TableauTraining #TableauCertification #DataVisualizationusingTableau #HowtovisualizedatainTableau #TableauDataVisualization #TableauTraining #DataVisualization #TableauCourse#tableauCharts #tableauTraining #tableauTutorial #tableauCertification #tableauDataanalysis #tableauvisualization #tableau Do you wish to learn more ? Are you looking for something more ? Enroll to Learnbay Data Science Certification Course. This #DataScience with Python course will teach you how to use Python to grasp data science and analytics approaches. You'll study the fundamentals of #Pythonprogramming and become an expert in data analytics, #machinelearning, data visualisation, web scraping, and natural language processing with our Python for Data Science Course. Many data science jobs require Python, so get a head start on your career with this interactive, hands-on training. Subscribe to our channel for concepts relating to machine learning : https://www.youtube.com/channel/UC-nt... To learn in detail about Data Visualization , data visualization using tableau , charts , graphs etc visit : https://bit.ly/3x6dbmS Watch detailed videos on : Python for data science : https://www.youtube.com/playlist?list=PLl1gyDCKkiQQ8Z-d5w4a22JKuKRPdjqNe Data science full course structure and syllabus : https://www.youtube.com/playlist?list=PLl1gyDCKkiQSfppJ4EPIDlvNPmPSKUQad Machine learning course : https://www.youtube.com/playlist?list=PLl1gyDCKkiQS3N0UqEtwAzG9l8l6eH_-0 Statistics for data science : https://www.youtube.com/playlist?list=PLl1gyDCKkiQRoADJw0YyvUdw9J9RSBjbJ Data visualization detailed course : https://youtu.be/cfYKC0l-ODg https://youtu.be/VRAqpvPdMHk Learn more: http://bit.ly/DatascienceCourse For more updates on courses/data science, artificial intelligence, machine learning check out : Linkedin : https://www.linkedin.com/company/10037723/admin/ Facebook : https://www.facebook.com/learnbay Instagram : https://www.instagram.com/learnbay_datascience/ Website : https://www.learnbay.co/data-science-course/data-science-and-ai/

Machine Learning & AI How to learn with no background | for Free | Explained

Learn ML and AI without affecting your current job. Learn from scratch with no investment.

NVIDIA’s Face Generator AI: This Is The Next Level! 👩‍🔬

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Alias-Free GAN" is available here: https://ift.tt/35LCFuf 📝 Our material synthesis paper is available here: https://ift.tt/2HhNzx5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m #nvidia

Transfer learning and Transformer models (ML Tech Talks)

In this session of Machine Learning Tech Talks, Software Engineer from Google Research, Iulia Turc, will walk us through the recent history of natural language processing, including the current state of the art architecture, the Transformer. 0:00 - Intro 1:07 - Encoding text 8:21 - Language modeling & transformers 29:46 - Transfer learning & BERT 43:55 - Conclusion Catch more ML Tech Talks → http://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow

Neural Materials Are Amazing! 🔮

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2S5tXnb ❤️ Their mentioned post is available here: https://ift.tt/2JVwmNm 📝 The paper "NeuMIP: Multi-Resolution Neural Materials" is available here: https://ift.tt/2UxCBfI 📝 Our latent space technique: https://ift.tt/2HhNzx5 📝 Our “Photoshop” technique: https://ift.tt/2EytbF6 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join 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

Building a ranking model with TF Recommenders (Building recommendation systems with TensorFlow)

Following our last video of building a retrieval system using TensorFlow Recommenders, we are going to build a ranking system using TensorFlow Recommenders in this video. Building a ranking model using TensorFlow Recommenders → https://goo.gle/3kFkmj1 Watch more Building recommendation systems with TensorFlow → https://goo.gle/3Bi8NUS Subscribe to TensorFlow → https://goo.gle/TensorFlow

Robotics and AI Learning Program|| Day - 7 Session || Bharat AI Labs

Display Learners' Projects which they have created using Scratch Programing, and taught about tutorials projects in scratch.

Part 1 of Deployment of Machine Learning Model on Google AI Platform made simple

#python #googlecloudaiplatform #simpleguidetodeploy #simpleapicall #gcp #machinelearning #regression

Part 2 of Deployment of Machine Learning Model on Google AI Platform made simple

#python #googlecloudaiplatform #simpleguidetodeploy #simpleapicall #gcp #machinelearning #regression

Machine Learning I Machine Learning Course in 5 minutes I Machine learning tutorial

@Communication Competence According to Forbes, Netflix saved $1 billion in 2017 as a result of its machine learning algorithm which recommends personalized TV shows and movies to subscribers and Bloomberg claims that the accuracy of Google AI’s machine learning algorithm in predicting a patients’ death is 95%. With these mind-blowing facts and figures, Machine Learning is bound to become an integral part of our everyday lives. An uber-talented Mechanical Engineer, Muhammad Shehryar Obaid 2020-Me-57, presents an introduction to machine learning in this less than 5 minutes video. #muhammadshehryarobaid2020me57 #machinelearning #moocs

Train Your Deep Learning Network Online | AI Made Simple

Tutorial on training a deep learning network with a GPU on a virtual machine. Includes a toy example of a MobileNet built in Tensorflow. Terminal commands: cd drive/MyDrive !git clone https://github.com/majdjamal/YT_deeplearning.git cd YT_tutorial ls !python3 train.py _________________________ Attributions: Music by https://www.bensound.com/ Thumbs up .png by https://creativecommons.org/licenses/by/4.0/

A Simulation That Looks Like Reality! 🤯

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "Solid-Fluid Interaction with Surface-Tension-Dominant Contact" is available here: https://ift.tt/2VSKm05 ❤️ 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, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join 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

Introduction to Explainable AI (ML Tech Talks)

This talk introduces the field of Explainable AI, outlines a taxonomy of ML interpretability methods, walks through an implementation deepdive of Integrated Gradients, and concludes with discussion on picking attribution baselines and future research directions. Chapters: 00:00 - Intro 2:31 - What is Explainable AI? 8:40 - Interpretable ML methods 14:52 - Deepdive: Integrated Gradients (IG) 39:13 - Picking baselines and future research directions Resources: Integrated gradients → https://goo.gle/2PxfRtq Vertex AI → https://goo.gle/3ifu7S5 What-if-tool → https://goo.gle/3ehZWbZ Catch more ML Tech Talks → http://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow

[ML News] Facebook AI adapting robots | Baidu autonomous excavators | Happy Birthday EleutherAI

A look into the happenings of the Machine Learning world. OUTLINE: 0:00 - Intro 0:25 - Facebook AI trains rapidly adapting robots 3:05 - Baidu presents autonomous excavator system 4:45 - EleutherAI turns 1 6:05 - Elon Musk says FSD harder than expected 8:10 - AI interview tools still fall short 11:10 - RunwayML AI-powered cloud video editor 11:55 - MineRL BASALT competition to learn from human feedback 13:15 - The Myth of the Expert Reviewer 15:55 - NVIDIA unveils Cambridge-1 supercomputer 17:10 - CLIP art sees rapid improvements 19:00 - AI demystifies boiling 21:20 - AI avatars for easier language learning 23:20 - Outro References: Facebook AI trains rapidly adapting robots https://ift.tt/3k5Bazt https://ift.tt/36lzucT Baidu presents autonomous excavator system https://ift.tt/3ykeMX5 https://www.youtube.com/watch?v=KFcNf_k0E_M EleutherAI turns 1 https://ift.tt/2TRGxaP Elon Musk says FSD is harder than expected https://ift.tt/3Au27Cx AI interview tools still fall short https://ift.tt/3hmi3zh RunwayML AI-powered cloud video editor https://runwayml.com/ MineRL BASALT competition to learn from human feedback https://ift.tt/3dSCVMi The Myth of the Expert Reviewer https://ift.tt/3yz1gip NVIDIA unveils Cambridge-1 supercomputer https://ift.tt/3hpJVRT https://ift.tt/3hr5PWp CLIP art sees rapid improvements https://ift.tt/2UWXbWH AI demystifies boiling https://ift.tt/3honBcF AI avatars for easier language learning https://ift.tt/3yy8rap Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick 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 BiliBili: https://ift.tt/3mfyjkW 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

Intro to TensorFlow Recommenders (Building recommendation systems with TensorFlow)

In this video, we will introduce you to TensorFlow Recommenders, an elegant and powerful library for building recommendation systems. We will first explore MovieLens dataset and then show you how to use TensorFlow Recommenders to build a basic retrieval model. TensorFlow Recommenders homepage → https://goo.gle/2IJAkrK TensorFlow Recommenders repository on GitHub → https://goo.gle/3ef3tYy Building a retrieval model using TensorFlow Recommenders → https://goo.gle/3yUAqkK Watch more Coding TensorFlow → https://goo.gle/Coding-TensorFlow Subscribe to TensorFlow → https://goo.gle/TensorFlow

This Magical AI Makes Your Photos Move! 🤳

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd  📝 The paper "Endless Loops: Detecting and Animating Periodic Patterns in Still Images" is available here: https://ift.tt/3bCnS8m 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, 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

Python for Beginners Tutorial #14 - Objects and Classes

In this video, you will learn about objects and classes in python! This tutorial is for beginners with absolutely no programming experience. Python is a great language to get started programming with! It is easy to learn and has a ton of applications, including AI/Machine Learning, Web Development, Web Scraping, Scripting, Game Development, and many more... If you found this video helpful, please like and subscribe! Thank you! Python Website: https://www.python.org/ Visual Studio Code: https://code.visualstudio.com

4 pixel cam AI - Machine Learning in Houdini Tutorial

I recommend watching this video first : https://youtu.be/ILsA4nyG7I0 Python 3.7.4 : https://www.python.org/downloads/release/python-374/ Pytorch : https://pytorch.org/get-started/locally/ Cuda 11.1: https://developer.nvidia.com/cuda-11.1.0-download-archive CuDNN : https://developer.nvidia.com/rdp/cudnn-archive hipfile : https://drive.google.com/file/d/1mbSSmV1ZTmmGoaFs8kkPQlPXCGQNbrt6/view?usp=sharing

Python for Beginners Tutorial #14 - Objects and Classes

In this video, you will learn about objects and classes in python! This tutorial is for beginners with absolutely no programming experience. Python is a great language to get started programming with! It is easy to learn and has a ton of applications, including AI/Machine Learning, Web Development, Web Scraping, Scripting, Game Development, and many more... If you found this video helpful, please like and subscribe! Thank you! Python Website: https://www.python.org/ Visual Studio Code: https://code.visualstudio.com

4 pixel cam AI - Machine Learning in Houdini Tutorial

I recommend watching this video first : https://youtu.be/ILsA4nyG7I0 Python 3.7.4 : https://www.python.org/downloads/release/python-374/ Pytorch : https://pytorch.org/get-started/locally/ Cuda 11.1: https://developer.nvidia.com/cuda-11.1.0-download-archive CuDNN : https://developer.nvidia.com/rdp/cudnn-archive hipfile : https://drive.google.com/file/d/1mbSSmV1ZTmmGoaFs8kkPQlPXCGQNbrt6/view?usp=sharing

I'm taking a break

I'll be back, don't worry :) Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick 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/3qcgOFy BiliBili: https://ift.tt/3mfyjkW 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

This AI Helps Testing The Games Of The Future! 🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2S5tXnb ❤️ Their mentioned post is available here: https://ift.tt/3ic5LsL 📝 The paper "Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents" is available here: https://ift.tt/3hxQx1K 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, 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 #gamedev

AI in Azure Part 3 - Machine Learning Services

In this Video I discuss the Azure Machine Learning Services. I will cover the AML Studio, Auto ML, Responsible ML, Architecture and the user experience. https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml#automl-in-azure-machine-learning https://www.microsoft.com/en-us/research/project/automl/ https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-environment#local #AI #Azure #MachineLearning #ArtificialIntelligence

OCIML 2021 Sesi I (Part 2) - Pengenalan dan Implementasi AI - Machine Learning dalam Kedokteran

OCIML 2021 Sesi I - Pengenalan dan Implementasi AI - Machine Learning dalam Kedokteran (Junaidillah Fadlil Samsung R&D)

AI in Azure Part 3 - Machine Learning Services

In this Video I discuss the Azure Machine Learning Services. I will cover the AML Studio, Auto ML, Responsible ML, Architecture and the user experience. https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml#automl-in-azure-machine-learning https://www.microsoft.com/en-us/research/project/automl/ https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-environment#local #AI #Azure #MachineLearning #ArtificialIntelligence

Machine Learning with AI Using Python Live Online Training Day 11| APPWARS Technologies Pvt. Ltd. |

What is Deep Learning? | Deep Learning Tutorial For Beginners | Edureka | Deep Learning Rewind - 2

🔥Edureka Tensorflow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow This Edureka "What is Deep Learning" video will help you to understand the relationship between Deep Learning, Machine Learning and Artificial Intelligence. It will also explain what is Deep learning and how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE ------------------------------------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 --------------------------------------------------------------- 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?country=in Meetup: https://www.meetup.com/edureka/ #edureka #deeplearningEdureka #whatisdeeplearning #deeplearningTutorial #learnDeeplearning #withMe - - - - - - - - - - - - - - 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. - - - - - - - - - - - - - - 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 3. Business Analysts 4. Information Architects 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Python Tutorial for Beginners #13 - List Methods

In this video, you will learn how to use list methods/functions in python! This tutorial is for beginners with absolutely no programming experience. Python is a great language to get started programming with! It is easy to learn and has a ton of applications, including AI/Machine Learning, Web Development, Web Scraping, Scripting, Game Development, and many more... If you found this video helpful, please like and subscribe! Thank you! Python Website: https://www.python.org/ Visual Studio Code: https://code.visualstudio.com

[ML News] GitHub Copilot - Copyright, GPL, Patents & more | Brickit LEGO app | Distill goes on break

#copilot #copyright #gpl GitHub and OpenAI release Copilot, an AI-powered code autocomplete system that can generate entire functions, classes, and modules from mere definitions and docstrings. Copilot was trained on all public GitHub repositories, and this has a lot of people upset about questions on copyright, code licenses, social obligations, and how much you can profit from other people's work. I give my opinions on the issue in relation to copyright law, the GPL license, and terms of service. Further, we discuss the Brickit app to organize your LEGOs, Distill going on a break, and much more. OUTLINE: 0:00 - Intro 0:20 - GitHub Copilot 6:55 - My opinion on Copilot & Copyright 17:25 - Facebook AI image similarity challenge 18:00 - Brickit app scans your LEGOs and suggests builds 18:40 - Distill journal goes on break 19:50 - Amazon uses algorithms to hire & fire Flex drivers 23:20 - Helpful Libraries: TF Decision Forests, Habitat, Falken, Brax 24:20 - AI-generated papers give science a hard time References: GitHub Copilot: AI pair programmer https://twitter.com/gdb/status/1409890354132750336 https://twitter.com/rickhanlonii/status/1410020702028193798 https://ift.tt/2ThYlLY https://ift.tt/3xbvw2C https://ift.tt/36liLGL https://ift.tt/3jQKJSO https://ift.tt/36n8qKw https://ift.tt/3jVyGU5 https://ift.tt/3AHKNdl https://twitter.com/giffmana/status/1410320795222654981 https://twitter.com/search?q=copilot&src=typed_query&f=image Facebook AI launches image similarity challenge https://ift.tt/3zHyzRN Brickit app sorts your LEGOs https://ift.tt/2TNbMDS https://ift.tt/36kZyFn Distill goes on break https://ift.tt/3AmweMg Amazon uses Algorithms to fire Flex drivers https://ift.tt/2THX5SG TensorFlow decision forests https://ift.tt/3uquqxB Facebook AI habitat 2.0 https://ift.tt/3yaG6GW Google Falken trains game-playing agents https://ift.tt/3h6dXvf https://ift.tt/3AoBhfg Google Brax: differentiable physics simulator https://ift.tt/3uLBbu1 https://ift.tt/3jWi26Q Fake science is getting faker https://ift.tt/3dFyuVc Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick 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 BiliBili: https://ift.tt/3mfyjkW 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

NVIDIA’s GANCraft AI: Feels Like Magic! 🌴 …Also, 1 Million Subs! 🥳

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Unsupervised 3D Neural Rendering of Minecraft Worlds" is available here: https://ift.tt/3dn4q0Y ❤️ 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, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, 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 #minecraft #gancraft

Creating People And Other Stuff With AI/Machine Learning... Creepy But Fascinating...

**Buy software directly at PredCaliber.com** http://www.predcaliber.com/software Win 10 Pro x64 Retail now only $12,50! **This is NOT a sponsored video! I just really like Artbreeder :) ** Today something unique at my channel, a website! Artbreeder. I do notice that I say AI a lot, but most of Artbreeder is machine learning. (not sure of there is a huge difference) Artbreeder, formerly known as Ganbreeder, is a collaborative, machine learning-based art website. Using the models StyleGAN and BigGAN, the app allows users to generate and modify images of faces, landscapes, and paintings, among other categories. Go to: https://www.artbreeder.com/ to check it out for yourself!

What is Deep Learning? | Deep Learning Tutorial For Beginners | Edureka | Deep Learning Rewind - 2

🔥Edureka Tensorflow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow This Edureka "What is Deep Learning" video will help you to understand the relationship between Deep Learning, Machine Learning and Artificial Intelligence. It will also explain what is Deep learning and how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE ------------------------------------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 --------------------------------------------------------------- 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?country=in Meetup: https://www.meetup.com/edureka/ #edureka #deeplearningEdureka #whatisdeeplearning #deeplearningTutorial #learnDeeplearning #withMe - - - - - - - - - - - - - - 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. - - - - - - - - - - - - - - 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 3. Business Analysts 4. Information Architects 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Content-based filtering & collaborative filtering (Building recommendation systems with TensorFlow)

In this video we will be walking you through the concepts of content-based filtering and collaborative filtering, which are traditional algorithms for recommendation systems but are useful to help us better understand modern recommenders. Recommendation systems on Google Developers website → https://goo.gle/3yx9XK9 Building a recommendation model using Stochastic Gradient Descent → https://goo.gle/2SNAm70 Neural Collaborative Filtering implementation in TensorFlow Model Garden → https://goo.gle/3qSrEBg Watch more Coding TensorFlow → https://goo.gle/Coding-TensorFlow Subscribe to TensorFlow → https://goo.gle/TensorFlow

Introduction to A.I & D.L - Day-1 | Deep Learning Master Class 5 Day webinar

Artificial Intelligence Internship: https://www.instamojo.com/pantechsolutions/ai-master-class-using-python/?discount=season3 Machine Learning Internship: https://www.instamojo.com/pantechsolutions/machine-learning-master-class/?discount=season3 Machine Learning Internship (10X Value than YouTube) Attendance:https:https://docs.google.com/forms/d/e/1FAIpQLSflZNDsfbKmhogqyupXiexASanhOOAbFUv-IJTk6c_V9vBrTw/viewform Application-based Learning Learn Faster & Easier than You Think 🚀 🔥 A.I Facebook Community: www.facebook.com/groups/aimasterclasspython/ Instagram: https://www.instagram.com/invites/contact/?i=1mb5hugnzxnca&utm_content=2kkods8 Connect with the Speaker: https://www.linkedin.com/in/sanjay-kumar-a-p-4447801a5/

XOR ML Example - AI & Machine Learning Workshop: The Tutorial before your Tutorial - Part 8

#MachineLearningTutorial #AI #MachineLearning #Tutorial #ScienceandTechnology #ArtificialIntelligence #TensorFlow #Keras #SupervisedLearning #NeuralNetworks #Perceptron #Backpropagation #AND #XOR #DeepLearning #Backpropagation **This video is best viewed with the 1080p60 HD setting Checkout out Part 1 of this series: https://youtu.be/poQp5N2flOw Checkout out Part 2 of this series: https://youtu.be/3R1ahtudvbM Checkout out Part 3 of this series: https://youtu.be/97CiAjqbCpU Checkout out Part 4 of this series: https://youtu.be/y7_UTqwx5Y0 Checkout out Part 5 of this series: https://youtu.be/9sBj6qcauLU Checkout out Part 6 of this series: https://youtu.be/6AYig0h5klY Checkout out Part 7 of this series: https://youtu.be/gMv91zngjLU GitHub Google Colaboratory notebooks Project_Template: https://github.com/BlackMagicAI/AI-ML-Workshop/blob/master/notebooks/Project_Template.ipynb Completed XOR Example Project: https://github.com/BlackMagicAI/AI-ML-Workshop/blob/master/notebooks/xor_project.ipynb 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.Topics & Recap of Part 7 - (0:19) 2. XOR Solution Using A Neural Network - (2:01) 3. Machine Learning Workflow - (2:55) 4. XOR Project Tools - (3:59) 4. Open Google Colaboratory files - (4:37) 5. Library Imports - (9:05) 6. Data Preparation - (13:42) 7. Feature Extraction - (18:36) 8. Build Model - (28:48) 9. Train Model - (41:55) 10. Evaluate Model - (47:00) 11. Inference – Make Predictions - (52.21) 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.

One Simulation Paper, Tons of Progress! 💇

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "Revisiting Integration in the Material Point Method" is available here: https://ift.tt/2UqkYy8 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, 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 BOOTCAMP DAY 3 Machine learning

This project is about mask detecting using machine learning in PictoBlox.

Machine Learning with AI using Python Day 9 Live Recording Training| APPWARS Technologies

Python Tutorial for Beginners #12 - String Methods

In this video, you will learn how to use string methods/functions in python! This tutorial is for beginners with absolutely no programming experience. Python is a great language to get started programming with! It is easy to learn and has a ton of applications, including AI/Machine Learning, Web Development, Web Scraping, Scripting, Game Development, and many more... If you found this video helpful, please like and subscribe! Thank you! Python Website: https://www.python.org/ Visual Studio Code: https://code.visualstudio.com

Self-driving from VISION ONLY - Tesla's self-driving progress by Andrej Karpathy (Talk Analysis)

#tesla #selfdriving #karpathy Tesla is pushing the state-of-the-art in full self-driving, and interestingly, they explicitly switch from having multiple different sensors to a vision-only system. We discuss the highlights of Andrej Karpathy's talk about Tesla's FSD system, how to label petabytes of data, how to sample edge-cases, how to train a neural network that has to work in real-time, and why moving to having only cameras is superior to multi-sensor approaches. OUTLINE: 0:00 - Intro & Overview 1:55 - Current Auto-Breaking system 3:20 - Full Self-Driving from vision only 4:55 - Auto-Labelling for collecting data 8:45 - How to get diverse data from edge-cases 12:15 - Neural network architecture 16:05 - Tesla's in-house supercomputer 17:00 - Owning the whole pipeline 18:20 - Example results from vision only 23:10 - Conclusion & Comments Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick 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 BiliBili: https://ift.tt/3mfyjkW 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

DigitalFUTURES - Artificial Intelligence in Architecture; Exploring GANs - session 4

DigitalFUTURES is pleased to announce the launch of InclusiveFUTURES, the world’s largest ever festival of architecture and computational design. This workshop is the first-ever DigitalFUTURES workshop that is held in Farsi. It is about Artificial Intelligence in architecture. The primary purpose of this workshop is to implement GANs in practice. It has consisted of three main parts: first, fundamentals of AI and DeepLearning.second, deep learning for computer vision, and the last part, Generative Adversarial Networks. ### LinkedIn Account https://www.linkedin.com/in/mohammed-behjoo-446098b7/ ### YouTube Channel https://www.youtube.com/channel/UCudI0gvPh-YbiY2zLM7im-w ### GitHub https://github.com/mohammedbehjoo

Python Tutorial for Beginners #12 - String Methods

In this video, you will learn how to use string methods/functions in python! This tutorial is for beginners with absolutely no programming experience. Python is a great language to get started programming with! It is easy to learn and has a ton of applications, including AI/Machine Learning, Web Development, Web Scraping, Scripting, Game Development, and many more... If you found this video helpful, please like and subscribe! Thank you! Python Website: https://www.python.org/ Visual Studio Code: https://code.visualstudio.com

Generative adversarial networks and TF-GAN (ML Tech Talks)

In this session of Machine Learning Tech Talks, Research Engineer Joel Shor will discuss a very cool development and technique in machine learning called Generative Adversarial Networks (GANs) and a library that offers open source to help make training and evaluating GANs easier. Chapters: 0:00 - Introduction 1:29 - Demos from Google 11:42 - What is a GAN? 27:14 - What are GANs good for? 41:59 - Deep dive: Metrics 54:05 - Deep dive: Self-attention GAN 57:47 - How to get started Resources: Boundless video → https://goo.gle/3AfOjf0 GANSynth project page → https://goo.gle/3xcm8vr Batch equalization paper → https://goo.gle/2TgkudL Superresolution colab → https://goo.gle/3y5xbqi Image-to-image translation colab → https://goo.gle/3hmwYrZ CycleGAN colab → https://goo.gle/3dwKyrG Inception Score implementation → https://goo.gle/3ydL322 Frechet Inception Distance implementation → https://goo.gle/2UMTfYo Self-Attention GAN implementation → https://goo.gle/3627o6q TF-GAN examples → https://goo.gle/3w3xvEE Catch more ML Tech Talks → http://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow