Eris Core Demo - Teensy 4.1 OS/Development Framework w/ integrated VM and AI/ML services.

Eris Core: https://github.com/bmonkaba/ERISCore#readme Wren: https://wren.io/ AI/ML: https://github.com/Fraunhofer-IMS/AIfES_for_Arduino#readme

AI/ML Recording #3

IT ARRIVED! YouTube sent me a package. (also: Limited Time Merch Deal)

LIMITED TIME MERCH DEAL: https://ift.tt/3o3zrMp 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 LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS 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

Quicknet Tutorial How To Build A Social Media Sentiment Analysis Machine Learning Model Without Code

Learn more at quicknet.ai to build your own machine learning model without code!

[ML News] ConvNeXt: Convolutions return | China regulates algorithms | Saliency cropping examined

#mlnews #convnext #mt3 Your update on what's new in the Machine Learning world! OUTLINE: 0:00 - Intro 0:15 - ConvNeXt: Return of the Convolutions 2:50 - Investigating Saliency Cropping Algorithms 9:40 - YourTTS: SOTA zero-shot Text-to-Speech 10:40 - MT3: Multi-Track Music Transcription 11:35 - China regulates addictive algorithms 13:00 - A collection of Deep Learning interview questions & solutions 13:35 - Helpful Things 16:05 - AlphaZero explained blog post 16:45 - Ru-DOLPH: HyperModal Text-to-Image-to-Text model 17:45 - Google AI 2021 Review References: ConvNeXt: Return of the Convolutions https://ift.tt/3GepqCU https://ift.tt/3HUFBG6 https://twitter.com/giffmana/status/1481054929573888005 https://twitter.com/wightmanr/status/1481150080765739009 https://twitter.com/tanmingxing/status/1481362887272636417 Investigating Saliency Cropping Algorithms https://ift.tt/3rQqo2o https://ift.tt/3nZh5fc https://ift.tt/32umbsn https://ift.tt/3q2CNR2 YourTTS: SOTA zero-shot Text-to-Speech https://ift.tt/33K2JbZ https://ift.tt/3KSJ1LQ https://ift.tt/3KK93R4 https://ift.tt/3ESylZ8 MT3: Multi-Track Music Transcription https://ift.tt/33WR75b https://ift.tt/3mN1CP2 https://ift.tt/3mGQCSX https://ift.tt/3FMP4i0 China regulates addictive algorithms https://ift.tt/3FWKb5T https://ift.tt/3FWtrvO A collection of Deep Learning interview questions & solutions https://ift.tt/3nXSjMG https://ift.tt/3HAEUBr Helpful Things https://ift.tt/31E1nyl https://ift.tt/3HLqnTv https://ift.tt/3nXCOUW https://ift.tt/3qYYou3 https://ift.tt/3KHYu16 https://ift.tt/3tZZCXY https://ift.tt/3JUqpuo https://mlcontests.com/ https://ift.tt/3H89iUc https://ift.tt/2JWgx6w https://ift.tt/3fTDlTB https://ift.tt/3n2Qbmn https://ift.tt/2YGAyV7 AlphaZero explained blog post https://ift.tt/3r8uLXz Ru-DOLPH: HyperModal Text-to-Image-to-Text model https://ift.tt/3f6Ac2m https://ift.tt/3tVX7WH Google AI 2021 Review https://ift.tt/3qg2RYW 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 LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS 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

[05x01] What is Machine Learning?

Machine Learning is an exciting new field that is growing rapidly. But what exactly is it? In this tutorial, you'll learn what Machine Learning is and you'll understand how it's related to Artificial Intelligence and Deep Learning. You'll also learn about the broad classifications of the Machine Learning algorithms: Supervised Learning, Unsupervised Learning and Reinforcement Learning. This is the first tutorial in a new 13-part series called Julia Machine Learning for Beginners. There is no coding in this introductory tutorial, so there are no prerequisites for this episode. However, for the rest of the tutorial series, knowledge of Julia and VS Code is required. - This tutorial is intended for students, hobbyists and amateurs. - This tutorial is episode 01 of a 13-part series and is part of the Julia Machine Learning for Beginners playlist. - Schedule: New tutorials are posted on Sundays / Mondays. - Prerequisites: none. 00:00 Intro 04:50 A Brief History of Artificial Intelligence 10:03 What is Artificial Intelligence? 13:36 What is Machine Learning? 23:01 Supervised Learning 25:27 Unsupervised Learning 27:51 Reinforcement Learning 29:17 What is Deep Learning? 31:54 Recap 33:31 Outro ######################################## # Links for this tutorial ######################################## # Julia for Beginners Playlist https://www.youtube.com/watch?v=0oChN11wf_4&list=PLhQ2JMBcfAsi_3g2AFJ6B84d8c5jw5kXp # Package development in VSCode | Workshop | JuliaCon 2021 (Sebastian Pfitzner) https://youtu.be/F1R3ETaRQXY ######################################## # Links for this series ######################################## # The Julia Programming Language https://julialang.org/ https://docs.julialang.org/en/v1/ https://www.youtube.com/c/TheJuliaLanguage # VS Code https://code.visualstudio.com/ ######################################## Notice of Non-Affiliation and Disclaimer: I am not affiliated, associated, authorized, endorsed by, or in any way officially connected with The Julia Programming Language, Julia Academy, Julia Computing, Microsoft, or any of their subsidiaries or their affiliates. Nor am I affiliated, associated, authorized, endorsed by, or in any way officially connected with any software, packages or libraries used in this video. All marks, emblems and images are registered trademarks of their respective owners. Use of them does not imply any affiliation with or endorsement by them. ######################################## Join Button (Channel Membership): If you like what I do, then please consider Joining and becoming a Channel Member. https://www.youtube.com/channel/UCQwQVlIkbalDzmMnr-0tRhw/join Thank you!

What is Neural Network & How It Works | Neural Network | Python NN | ANN | NN Python | ANN Python |

What is Neural Network & How It Works | Neural Network | Python NN | ANN | NN Python | ANN Python | Hello Guys, Join this channel to get access to perks: https://www.youtube.com/channel/UC7A5u12yVIZaCO_uXnNhc5g/join You Must Complete This Before You Go :- https://www.youtube.com/watch?v=yBJUrn2tggo&t=1608s Related Topics :- programming,developer,javascript,web development,coding,programmer,software developer,software development,react js,tutorial,yt:cc=on,artificial intelligence with python,artificial intelligence tutorial,AI Python,artificial intelligence,artificial intelligence python,artificial intelligence using python,Artificial Intelligence Tutorial using Python,applications of artificial intelligence,Introduction to Artificial Intelligence,What is Artificial Intelligence,Machine learning basics,edureka,python edureka,ai python tutorial,ai python examples,artificial intelligence edureka,What is Artificial Intelligence With Full Information?,What is Artificial Intelligence in Hindi?,Artificial Intelligence Kya Hain?,What is artificial intelligence with examples? What is artificial intelligence in computer?,Artificial intelligence Animation Video,Benefits of artificial intelligence,Artificial intelligence future,Who started AI?,Where is Artificial Intelligence used?,What is the disadvantage of Artificial Intelligence?,Types of artificial intelligence,artificial intelligence,artificial intelligence course,machine learning,machine learning course,ai and machine learning,ai and machine learning full course,ai and machine learning tutorial,ai and machine learning for beginners,artificial intelligence and machine learning,artificial intelligence and machine learning course,machine learning algorithms,artificial intelligence tutorial,ai and ml,artificial intelligence full course,ai full course,artificial intelligence tutorial,artificial intelligence tutorial for beginners,artificial intelligence lecture,artificial intelligence projects,artificial intelligence course,artificial intelligence for beginners,ai artificial intelligence,ai,ai tutorial,ai tutorial for beginners,ai tutorial python,artificial intelligence training videos,simplilearn artificial intelligence,simplilearn

Python Tutorials II for Machine Learning | machine learning video part 3

Don't forget to ask your doubts in comments section(if any) #machinelearning #pythonprogramming email: mitadrudutta12@gmail.com Channel Description : Hello guys this is your friend Mitadru Datta from IITkgp Channel launching from Jan 1 , 2022. Here we will discuss some complex competitive coding problems, concepts and Artificial Intelligence with Machine Learning, Deep Learning and Neural Networks.[No pre-requisites required for AI. I will be explaining basics of python too before starting with AI] Later, we would also be taking up App Development, FreeCAD and TinkerCAD projects Each video will be explained fully with proper code explanation. Would upload videos atleast once in two weeks apart from exceptional cases Copyright note : All the slides in the video are copyrighted

Dynamic Inference with Neural Interpreters (w/ author interview)

#deeplearning #neuralinterpreter #ai This video includes an interview with the paper's authors! What if we treated deep networks like modular programs? Neural Interpreters divide computation into small modules and route data to them via a dynamic type inference system. The resulting model combines recurrent elements, weight sharing, attention, and more to tackle both abstract reasoning, as well as computer vision tasks. OUTLINE: 0:00 - Intro & Overview 3:00 - Model Overview 7:00 - Interpreter weights and function code 9:40 - Routing data to functions via neural type inference 14:55 - ModLin layers 18:25 - Experiments 21:35 - Interview Start 24:50 - General Model Structure 30:10 - Function code and signature 40:30 - Explaining Modulated Layers 49:50 - A closer look at weight sharing 58:30 - Experimental Results Paper: https://ift.tt/33WbWxi Guests: Nasim Rahaman: https://twitter.com/nasim_rahaman Francesco Locatello: https://twitter.com/FrancescoLocat8 Waleed Gondal: https://twitter.com/Wallii_gondal Abstract: Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization Authors: Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf 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 LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS 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

What is Natural Language Processing & How It Works | NLP Python | NLP Using Python | Python | NLP |

What is Natural Language Processing & How It Works | NLP Python | NLP Using Python | Python | NLP | Hello Guys, Join this channel to get access to perks: https://www.youtube.com/channel/UC7A5u12yVIZaCO_uXnNhc5g/join You Must Complete This Before You Go :- https://www.youtube.com/watch?v=yBJUrn2tggo&t=1608s Related Topics :- programming,developer,javascript,web development,coding,programmer,software developer,software development,react js,tutorial,yt:cc=on,artificial intelligence with python,artificial intelligence tutorial,AI Python,artificial intelligence,artificial intelligence python,artificial intelligence using python,Artificial Intelligence Tutorial using Python,applications of artificial intelligence,Introduction to Artificial Intelligence,What is Artificial Intelligence,Machine learning basics,edureka,python edureka,ai python tutorial,ai python examples,artificial intelligence edureka,What is Artificial Intelligence With Full Information?,What is Artificial Intelligence in Hindi?,Artificial Intelligence Kya Hain?,What is artificial intelligence with examples? What is artificial intelligence in computer?,Artificial intelligence Animation Video,Benefits of artificial intelligence,Artificial intelligence future,Who started AI?,Where is Artificial Intelligence used?,What is the disadvantage of Artificial Intelligence?,Types of artificial intelligence,artificial intelligence,artificial intelligence course,machine learning,machine learning course,ai and machine learning,ai and machine learning full course,ai and machine learning tutorial,ai and machine learning for beginners,artificial intelligence and machine learning,artificial intelligence and machine learning course,machine learning algorithms,artificial intelligence tutorial,ai and ml,artificial intelligence full course,ai full course,artificial intelligence tutorial,artificial intelligence tutorial for beginners,artificial intelligence lecture,artificial intelligence projects,artificial intelligence course,artificial intelligence for beginners,ai artificial intelligence,ai,ai tutorial,ai tutorial for beginners,ai tutorial python,artificial intelligence training videos,simplilearn artificial intelligence,simplilearn

Noether Networks: Meta-Learning Useful Conserved Quantities (w/ the authors)

#deeplearning #noether #symmetries This video includes an interview with first author Ferran Alet! Encoding inductive biases has been a long established methods to provide deep networks with the ability to learn from less data. Especially useful are encodings of symmetry properties of the data, such as the convolution's translation invariance. But such symmetries are often hard to program explicitly, and can only be encoded exactly when done in a direct fashion. Noether Networks use Noether's theorem connecting symmetries to conserved quantities and are able to dynamically and approximately enforce symmetry properties upon deep neural networks. OUTLINE: 0:00 - Intro & Overview 18:10 - Interview Start 21:20 - Symmetry priors vs conserved quantities 23:25 - Example: Pendulum 27:45 - Noether Network Model Overview 35:35 - Optimizing the Noether Loss 41:00 - Is the computation graph stable? 46:30 - Increasing the inference time computation 48:45 - Why dynamically modify the model? 55:30 - Experimental Results & Discussion Paper: https://ift.tt/3mBSRH8 Website: https://ift.tt/3FOYPLW Code: https://ift.tt/3FHyoYn Abstract: Progress in machine learning (ML) stems from a combination of data availability, computational resources, and an appropriate encoding of inductive biases. Useful biases often exploit symmetries in the prediction problem, such as convolutional networks relying on translation equivariance. Automatically discovering these useful symmetries holds the potential to greatly improve the performance of ML systems, but still remains a challenge. In this work, we focus on sequential prediction problems and take inspiration from Noether's theorem to reduce the problem of finding inductive biases to meta-learning useful conserved quantities. We propose Noether Networks: a new type of architecture where a meta-learned conservation loss is optimized inside the prediction function. We show, theoretically and experimentally, that Noether Networks improve prediction quality, providing a general framework for discovering inductive biases in sequential problems. Authors: Ferran Alet, Dylan Doblar, Allan Zhou, Joshua Tenenbaum, Kenji Kawaguchi, Chelsea Finn 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 LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS 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 trained with self-play is unstoppable (Reinforcement Learning)

I used reinforcement learning and self-play to train an AI for a melee combat game Code: https://github.com/HarrisSteven/RL-Game

What is Natural Language Processing & How It Works | NLP Python | NLP Using Python | Python | NLP |

What is Natural Language Processing & How It Works | NLP Python | NLP Using Python | Python | NLP | Hello Guys, Join this channel to get access to perks: https://www.youtube.com/channel/UC7A5u12yVIZaCO_uXnNhc5g/join You Must Complete This Before You Go :- https://www.youtube.com/watch?v=yBJUrn2tggo&t=1608s Related Topics :- programming,developer,javascript,web development,coding,programmer,software developer,software development,react js,tutorial,yt:cc=on,artificial intelligence with python,artificial intelligence tutorial,AI Python,artificial intelligence,artificial intelligence python,artificial intelligence using python,Artificial Intelligence Tutorial using Python,applications of artificial intelligence,Introduction to Artificial Intelligence,What is Artificial Intelligence,Machine learning basics,edureka,python edureka,ai python tutorial,ai python examples,artificial intelligence edureka,What is Artificial Intelligence With Full Information?,What is Artificial Intelligence in Hindi?,Artificial Intelligence Kya Hain?,What is artificial intelligence with examples? What is artificial intelligence in computer?,Artificial intelligence Animation Video,Benefits of artificial intelligence,Artificial intelligence future,Who started AI?,Where is Artificial Intelligence used?,What is the disadvantage of Artificial Intelligence?,Types of artificial intelligence,artificial intelligence,artificial intelligence course,machine learning,machine learning course,ai and machine learning,ai and machine learning full course,ai and machine learning tutorial,ai and machine learning for beginners,artificial intelligence and machine learning,artificial intelligence and machine learning course,machine learning algorithms,artificial intelligence tutorial,ai and ml,artificial intelligence full course,ai full course,artificial intelligence tutorial,artificial intelligence tutorial for beginners,artificial intelligence lecture,artificial intelligence projects,artificial intelligence course,artificial intelligence for beginners,ai artificial intelligence,ai,ai tutorial,ai tutorial for beginners,ai tutorial python,artificial intelligence training videos,simplilearn artificial intelligence,simplilearn

What is Deep Learning & How It's Works | Deep Learning | Deep Learning Python | Python DL | Python |

What is Deep Learning & How It's Works | Deep Learning | Deep Learning Python | Python DL | Python | Hello Guys, Join this channel to get access to perks: https://www.youtube.com/channel/UC7A5u12yVIZaCO_uXnNhc5g/join You Must Complete This Before You Go :- https://www.youtube.com/watch?v=yBJUrn2tggo&t=1608s Related Topics :- programming,developer,javascript,web development,coding,programmer,software developer,software development,react js,tutorial,yt:cc=on,artificial intelligence with python,artificial intelligence tutorial,AI Python,artificial intelligence,artificial intelligence python,artificial intelligence using python,Artificial Intelligence Tutorial using Python,applications of artificial intelligence,Introduction to Artificial Intelligence,What is Artificial Intelligence,Machine learning basics,edureka,python edureka,ai python tutorial,ai python examples,artificial intelligence edureka,What is Artificial Intelligence With Full Information?,What is Artificial Intelligence in Hindi?,Artificial Intelligence Kya Hain?,What is artificial intelligence with examples? What is artificial intelligence in computer?,Artificial intelligence Animation Video,Benefits of artificial intelligence,Artificial intelligence future,Who started AI?,Where is Artificial Intelligence used?,What is the disadvantage of Artificial Intelligence?,Types of artificial intelligence,artificial intelligence,artificial intelligence course,machine learning,machine learning course,ai and machine learning,ai and machine learning full course,ai and machine learning tutorial,ai and machine learning for beginners,artificial intelligence and machine learning,artificial intelligence and machine learning course,machine learning algorithms,artificial intelligence tutorial,ai and ml,artificial intelligence full course,ai full course,artificial intelligence tutorial,artificial intelligence tutorial for beginners,artificial intelligence lecture,artificial intelligence projects,artificial intelligence course,artificial intelligence for beginners,ai artificial intelligence,ai,ai tutorial,ai tutorial for beginners,ai tutorial python,artificial intelligence training videos,simplilearn artificial intelligence,simplilearn

Python Tutorials I for Machine Learning | Machine Learning video part 2

#pythonprogramming #machinelearning This is the part 2 video of Machine Learning Series. Don't forget to ask your doubts in the comment section/email ! Before going on to core machine learning algorithms, I would be first completing with the python and mathematical background required. Don't forget the bell icon. Do like the video ! When I get some free time in my next semester, I will be making a website which would contain the slides and notes contained in my videos email : mitadrudutta12@gmail.com Description : Hello guys this is your friend Mitadru Datta from IITkgp Channel launching from Jan 1 , 2022. Here we will discuss some complex competitive coding problems, concepts and Artificial Intelligence with Machine Learning, Deep Learning and Neural Networks.[No pre-requisites required for AI. I will be explaining basics of python too before starting with AI] Later, we would also be taking up App Development, FreeCAD and TinkerCAD projects Each video will be explained fully with proper code explanation. Would upload videos atleast once in two weeks apart from exceptional cases Copyright note: All the slides in the video are copyrighted

Burnout Dominator Cinematic Tutorial 4K Remaster [AI Machine Learning]

Thanks to Kim2091 for the 4x_UltraSharp model. It still looks blurry at times but that's from the limitations of the source footage which was extremely compressed. I was surprised that the AI could recover this much detail in fact. I tried video interpolation with methods such as XVFI however my computer doesn't have enough VRAM at the moment so I will need to upgrade in that case. I attempted RIFE interpolation but the generated frames are blurry and reduce the overall video clarity. //////////////////// Video script ////////////////////// ESRGAN is a Generative Adversarial Network that reconstructs and upscales lower resolution images and textures into higher resolution counterparts while retaining most details. This video was created by extracting the FMV from the NTSC Burnout Dominator disc. This file was processed through ESRGAN with the 4x_ultrasharp training model. The output file was then processed with Adobe Premiere Pro. The video was decided not to be interpolated to a higher framerate with RIFE as it causes interpolation artefacts. The source material is heavily compressed and RIFE does not help improve clarity.

100 Day Challenge on Artificial Intelligence and Machine Learning #Shorts

100 Day Challenge on Artificial Intelligence and Machine Learning: 1. Projects 2. Tutorials 3. AI and ML Algorithms 4. Research papers 5. AI and ML news 6. Many more 7. Go to https://www.gopichandrakesan.com/100-day-challenge/ 8. Check all my 100-day challenge on AI and ML #shorts video on Have you ever tried the 100 Day Challenge on Artificial Intelligence and Machine Learning? #Shorts in less than 60 secs. 🔗 Social Medias🔗 🌎 Website: https://gopichandrakesan.com/ LinkedIn: https://www.linkedin.com/in/gopichandrakesan/ Facebook: https://www.facebook.com/RCGopiTechie Twitter: https://twitter.com/RCGopiTechie GitHub: https://github.com/rcgopi100 ✪Tags ✪ - MachineLearning - Artificial Intelligence - Deep Learning ✪ Hashtags ✪ #MachineLearning #ArtificialIntelligence #DeepLearning #AI #ML

100 Day Challenge on Artificial Intelligence and Machine Learning

100 Day Challenge on Artificial Intelligence and Machine Learning full video: 1. Projects 2. Tutorials 3. AI and ML Algorithms 4. Research papers 5. AI and ML news 6. Many more 7. Go to https://www.gopichandrakesan.com/100-day-challenge/ 8. Check all my 100-day challenge on AI and ML 🔗 Social Medias🔗 🌎 Website: https://gopichandrakesan.com/ LinkedIn: https://www.linkedin.com/in/gopichandrakesan/ Facebook: https://www.facebook.com/RCGopiTechie Twitter: https://twitter.com/RCGopiTechie GitHub: https://github.com/rcgopi100 ✪Tags ✪ - MachineLearning - Artificial Intelligence - Deep Learning ✪ Hashtags ✪ #100DaysChallenge #MachineLearning #ArtificialIntelligence #DeepLearning #AI #ML

Burnout Dominator Cinematic Tutorial 4K Remaster [AI Machine Learning]

Thanks to Kim2091 for the 4x_UltraSharp model. It still looks blurry at times but that's from the limitations of the source footage which was extremely compressed. I was surprised that the AI could recover this much detail in fact. I tried video interpolation with methods such as XVFI however my computer doesn't have enough VRAM at the moment so I will need to upgrade in that case. I attempted RIFE interpolation but the generated frames are blurry and reduce the overall video clarity. //////////////////// Video script ////////////////////// ESRGAN is a Generative Adversarial Network that reconstructs and upscales lower resolution images and textures into higher resolution counterparts while retaining most details. This video was created by extracting the FMV from the NTSC Burnout Dominator disc. This file was processed through ESRGAN with the 4x_ultrasharp training model. The output file was then processed with Adobe Premiere Pro. The video was decided not to be interpolated to a higher framerate with RIFE as it causes interpolation artefacts. The source material is heavily compressed and RIFE does not help improve clarity.

100 Day Challenge on Artificial Intelligence and Machine Learning #Shorts

100 Day Challenge on Artificial Intelligence and Machine Learning: 1. Projects 2. Tutorials 3. AI and ML Algorithms 4. Research papers 5. AI and ML news 6. Many more 7. Go to https://www.gopichandrakesan.com/100-day-challenge/ 8. Check all my 100-day challenge on AI and ML #shorts video on Have you ever tried the 100 Day Challenge on Artificial Intelligence and Machine Learning? #Shorts in less than 60 secs. 🔗 Social Medias🔗 🌎 Website: https://gopichandrakesan.com/ LinkedIn: https://www.linkedin.com/in/gopichandrakesan/ Facebook: https://www.facebook.com/RCGopiTechie Twitter: https://twitter.com/RCGopiTechie GitHub: https://github.com/rcgopi100 ✪Tags ✪ - MachineLearning - Artificial Intelligence - Deep Learning ✪ Hashtags ✪ #MachineLearning #ArtificialIntelligence #DeepLearning #AI #ML

AI for Space Day 4 - Introduction to Machine Learning (Session 2)

Day 4 of Introductory Machine Learning Webinar Series organized by SEDS Pera. Reach us on Email : sedsperachapter@gmail.com Website : https://sedspera.soc.pdn.ac.lk/ Facebook : https://www.facebook.com/sedspera LinkedIn : https://www.linkedin.com/company/seds-pera

Image Classification using CNN | Deep Learning Tutorial |Machine Learning|Image Classification 2022

In this video we will do small image classification using Dog and cat dataset in tensorflow. We will use convolutional neural network for this image classification problem. we will train a CNN and see how the model accuracy improves. This tutorial will help you understand why CNN .then we will learn how to work on custom dataset with multiple classes get accuracy more then 96%. For code : comment your Email Address. Model: CNN Accuracy: More then 96% code: Python softwate : Google Colab Number of Epochs : 50 References: https://ieeexplore.ieee.org/document/8776982 https://link.springer.com/chapter/10.1007/978-3-319-16841-8_52 https://www.researchgate.net/publication/275257620_Image_Classification_Using_Convolutional_Neural_Networks https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3833453 Links for installing Libreries: Install Tensorflow: https://pypi.org/project/tensorflow/ install Keras: https://pypi.org/project/keras/ if you want further support contact us : http://www.smartaitechnologies.com/ For code and further support give your email in comments.

This AI Creates Lava From Water…Sort Of! 🌊

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd  📝 The paper "VGPNN: Diverse Generation from a Single Video Made Possible" is available here: https://ift.tt/3CqKFz8 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, 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, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Peter Edwards, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Wish to watch these videos in early access? Join us here: https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/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

DSA-CS Korea 2020 AI, 2nd-3

Python - Packages for ML and AI

Tutorial 5: Decision Tree in Machine learning #decision #decisiontree #machinelearning #tutorial

hello everyone, AI, Data science l, machine learning, Deep learning, natural language processing with python, podcast on AI, Tech News and quantitative aptitude etc for free. Follow the below link:https://youtube.com/c/CharSi26 Please support channel: Phone pe: 7013826548@ybl GPay : vamsidharreddy2219@oksbi Subscribe my another channel: https://youtube.com/channel/UCzcpqSMm1G9BMFTlK2txlww make sure like and subscribe 🙂😉

Tutorial 5: Decision Tree in Machine learning #decision #decisiontree #machinelearning #tutorial

hello everyone, AI, Data science l, machine learning, Deep learning, natural language processing with python, podcast on AI, Tech News and quantitative aptitude etc for free. Follow the below link:https://youtube.com/c/CharSi26 Please support channel: Phone pe: 7013826548@ybl GPay : vamsidharreddy2219@oksbi Subscribe my another channel: https://youtube.com/channel/UCzcpqSMm1G9BMFTlK2txlww make sure like and subscribe 🙂😉

How to learn Data science and Machine Learning Tutorial for Beginners

Click to Subscribe- https://bit.ly/3hiqfAA How to learn Data science and Machine Learning Tutorial for Beginners

Player of Games: All the games, one algorithm! (w/ author Martin Schmid)

#playerofgames #deepmind #alphazero Special Guest: First author Martin Schmid (https://twitter.com/Lifrordi) Games have been used throughout research as testbeds for AI algorithms, such as reinforcement learning agents. However, different types of games usually require different solution approaches, such as AlphaZero for Go or Chess, and Counterfactual Regret Minimization (CFR) for Poker. Player of Games bridges this gap between perfect and imperfect information games and delivers a single algorithm that uses tree search over public information states, and is trained via self-play. The resulting algorithm can play Go, Chess, Poker, Scotland Yard, and many more games, as well as non-game environments. OUTLINE: 0:00 - Introduction 2:50 - What games can Player of Games be trained on? 4:00 - Tree search algorithms (AlphaZero) 8:00 - What is different in imperfect information games? 15:40 - Counterfactual Value- and Policy-Networks 18:50 - The Player of Games search procedure 28:30 - How to train the network? 34:40 - Experimental Results 47:20 - Discussion & Outlook Paper: https://ift.tt/3rKFiZF Abstract: Games have a long history of serving as a benchmark for progress in artificial intelligence. Recently, approaches using search and learning have shown strong performance across a set of perfect information games, and approaches using game-theoretic reasoning and learning have shown strong performance for specific imperfect information poker variants. We introduce Player of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning. Player of Games is the first algorithm to achieve strong empirical performance in large perfect and imperfect information games -- an important step towards truly general algorithms for arbitrary environments. We prove that Player of Games is sound, converging to perfect play as available computation time and approximation capacity increases. Player of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker (Slumbot), and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning. Authors: Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, Zach Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling 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 LinkedIn: https://ift.tt/3qcgOFy BiliBili: https://ift.tt/3nlqFZS 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