Want to learn A.I. Art? - Here's THE ULTIMATE Disco Diffusion Tutorial

#ChristML = AI Machine Learning = NewGawd I'm Denver Comedian Joshua Grambo, Thanks for being Freeroll Fam!! https://youtu.be/Un919FN40UQ ☝️Laugh at me! It helps support the channel ☝️ Venmo $1? Venmo@JoshGrambo Insta @JoshGrambo Twitter@JoshGrambo Tiktok@JoshGrambo Thanks AGAIN for being our Freeroll Family!! ☝️.......................Notebook......................................☝️ https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb?authuser=3#scrollTo=DiffuseTop .................................................................................. #aiart #ai #digitalart #digitaldrawing #photopaintings #VQGAN #CLIP #OpenAI #Stylegan #GPT #colab #machinelearning #Generative #DiscoDifusion #Latentdiffusion #dallE #dalle2 #VQGANCLIP #JoshGrambo

Machine Learning - RPG AI Team Learns to Defeat Dragon Boss

Shoutout to the following: Sebastian Lague: https://www.youtube.com/c/SebastianLague Dungeon Mason: https://assetstore.unity.com/publishers/23554 Code Monkey: https://www.youtube.com/channel/UCFK6NCbuCIVzA6Yj1G_ZqCg Source Code: https://github.com/XanderKehoe/RpgBossFightMachineLearning/tree/master

Resume Screening | TFIDFVectorizer | KNearestNeighbour | Python | Machine Learning | NLP tutorial

AI can rapidly process huge volumes of data, which makes it a most valuable tool for high-volume recruitment processes. Intelligent resume screening improves the quality of hire because it reduces human errors, unconscious bias and can make predictions. Please find the complete playlist for quantum computing https://www.youtube.com/watch?v=qMT_WoV9Yxg&list=PLdpmGL_ZpofW-4BpFZcoL4XWvxHx2TCon Please find the complete playlist for NLP ( Natural language Processing) https://www.youtube.com/watch?v=obQ_28gdoOg&list=PLdpmGL_ZpofX9za3QxI7VMRCODd6bqmpU Please find the complete playlist for speech recognition https://www.youtube.com/watch?v=D3JyZFkoJEE&list=PLdpmGL_ZpofVjHY4h8fv9etcGE7Z7H_hV Please find the complete playlist for deep learning below https://www.youtube.com/watch?v=IWoAEYNbQ-c&list=PLdpmGL_ZpofU-wdGcsUwdByrTCs9iNS_S please findthe complete playlist for backpropagation algorith below https://www.youtube.com/watch?v=TnG9JScUxCg&list=PLdpmGL_ZpofVNbMdrbVnAzOCZJdSpQleW please find the complete playlist for Gradient Descent algorithm below https://www.youtube.com/watch?v=S1d94WIg2yo&list=PLdpmGL_ZpofV_As-yjZ4IWx_CLDubHLjJ Please find the complete playlist for math below https://www.youtube.com/watch?v=rnQf-CVusE4&list=PLdpmGL_ZpofWBceEapT6Go6CgNGMjMu8Z Please find the complete playlist for statistics below https://www.youtube.com/watch?v=SwhPrVOcOzg&list=PLdpmGL_ZpofX52Txk3C09L_DtJQ0HqDbw Please find the complete playlist for supervised machine learning https://www.youtube.com/watch?v=6WyWz6ghrtU&list=PLdpmGL_ZpofWOVrXb5SVsXzNN2Wj2xh8a

Day 17 / 30 Artificial Intelligence Master Class series

If You Haven't Register still, Register Now: https://forms.gle/LdYDu9BuFNaZDvPs6 Internship on A.I - https://rzp.io/l/Coz25rEn 3 in 1 combo A.I + M.L + D.A - https://rzp.io/l/lDbvqkuE3B 📅 Event Date : 9th May - 7 th June ⏰ Event Timing : 5 P.M - 6 P.M 👨‍🏫Connect with Course Instructor : A P Sanjay Kumar - https://www.linkedin.com/in/sanjaykumar-ap Attendance link : https://docs.google.com/forms/d/e/1FAIpQLSeH4_gIfeaN7VCVWGZF2xI2uJiRmZ731NwWmfzrjKfMhzYH-Q/viewform?usp=sf_link Join Facebook Group : https://www.facebook.com/groups/404806661234261/ Join Telegram group : https://t.me/+YYCcql3YTK82OTNl What you will Learn on This 30 Days Master Class Series ✅DAY – 1 Overview of this course | Introduction to AI | How to create basic AI application (Chat bot using DialogFlow) ✅DAY – 2 How to install Python & Libraries | Basics of python Programming for AI. COMPUTER VISION ✅DAY – 3 Introduction to Computer Vision| How to install computer vision libraries ✅DAY – 4 Moving Object Detection and tracking using OpenCV ✅DAY – 5 Face Detection and Tracking using OpenCV ✅DAY – 6 Object Tracking based on color using OpenCV ✅DAY – 7 Face Recognition using OpenCV ✅DAY – 8 Face Emotion recognition using 68-Landmark Predictor OpenCV DEEP LEARNING ✅DAY – 9 Introduction to Deep learning | How to install DL libraries ✅DAY – 10 Designing your First Neural Network ✅DAY – 11 Object recognition from Pre-trained model ✅DAY – 12 Image classification using Convolutional Neural Network ✅DAY – 13 Hand gesture recognition using Deep Learning ✅DAY – 14 Leaf disease detection using Deep Learning ✅DAY – 15 Character recognition using Convolutional Neural Network ✅DAY – 16 Label reading using Optical Character recognition ✅DAY – 17 Smart Attendance system using Deep Learning ✅DAY – 18 Vehicle detection using Deep Learning ✅DAY – 19 License plate recognition using Deep Learning ✅DAY – 20 Drowsiness detection using Deep Learning ✅DAY – 21 Road sign recognition using Deep Learning MACHINE LEARNING ✅DAY – 22 Introduction to Machine learning| How to install ML libraries ✅DAY – 23 Evaluating and Deploying the various ML model ✅DAY – 24 Fake news detection using ML ✅DAY – 25 AI snake game design using ML NATURAL LANGUAGE PROCESSING ✅DAY – 26 Introduction to NLP & it’s Terminology | How to install NLP Libraries NLTK ✅DAY – 27 Title Formation from the paragraph design using NLP ✅DAY – 28 Speech emotion analysis using NLP DEPLOYING AI IN HARDWARE ✅DAY – 29 Cloud-based AI, Object recognition using Amazon Web Service (AWS) & Imagga ✅DAY – 30 Deploying AI application in Raspberry Pi with Neural Compute stick & Nvidia Jetson Nano Happy Learning M.K Jeevarajan DIRECTOR PANTECH PROLABS INDIA https://www.linkedin.com/in/jeevarajan/ For Queries: https://wa.me/+919363083283

AI Journeys : Unsupervised Learning

"The smarter the humans get, the lesser their brains has to work" There's hardly any field booming with as much exuberance as Artificial intelligence and machine learning lately, and for a good reason at that, because the smarter humans get, the closer humanity comes at mimicking the vast intricacies of the mind. Join VITMAS at this celebration of knowledge, at "AI JOURNEYS" part 2 where we screw open the mind of a machine for you and gain a better understanding of it through this "mind opening" experience

Resume Screening | TFIDFVectorizer | KNearestNeighbour | Python | Machine Learning | NLP tutorial

AI can rapidly process huge volumes of data, which makes it a most valuable tool for high-volume recruitment processes. Intelligent resume screening improves the quality of hire because it reduces human errors, unconscious bias and can make predictions. Please find the complete playlist for quantum computing https://www.youtube.com/watch?v=qMT_WoV9Yxg&list=PLdpmGL_ZpofW-4BpFZcoL4XWvxHx2TCon Please find the complete playlist for NLP ( Natural language Processing) https://www.youtube.com/watch?v=obQ_28gdoOg&list=PLdpmGL_ZpofX9za3QxI7VMRCODd6bqmpU Please find the complete playlist for speech recognition https://www.youtube.com/watch?v=D3JyZFkoJEE&list=PLdpmGL_ZpofVjHY4h8fv9etcGE7Z7H_hV Please find the complete playlist for deep learning below https://www.youtube.com/watch?v=IWoAEYNbQ-c&list=PLdpmGL_ZpofU-wdGcsUwdByrTCs9iNS_S please findthe complete playlist for backpropagation algorith below https://www.youtube.com/watch?v=TnG9JScUxCg&list=PLdpmGL_ZpofVNbMdrbVnAzOCZJdSpQleW please find the complete playlist for Gradient Descent algorithm below https://www.youtube.com/watch?v=S1d94WIg2yo&list=PLdpmGL_ZpofV_As-yjZ4IWx_CLDubHLjJ Please find the complete playlist for math below https://www.youtube.com/watch?v=rnQf-CVusE4&list=PLdpmGL_ZpofWBceEapT6Go6CgNGMjMu8Z Please find the complete playlist for statistics below https://www.youtube.com/watch?v=SwhPrVOcOzg&list=PLdpmGL_ZpofX52Txk3C09L_DtJQ0HqDbw Please find the complete playlist for supervised machine learning https://www.youtube.com/watch?v=6WyWz6ghrtU&list=PLdpmGL_ZpofWOVrXb5SVsXzNN2Wj2xh8a

Technical Topic Tuesday -59- Machine Learning #machinelearning #datascience #technology #ai #ml

​@Passion, People & Purpose #machinelearning #datascience #technology #ai #ml Machine Learning Purpose: to help enhance not only many industrial, enterprise & professional processes but also our everyday living What? a subset of artificial intelligence focused on building systems that can learn from historical data, identify patterns, & make logical decisions with little to no human intervention How? 1. Collecting and preparing the Data 2. Choosing a Model 3. Training the Model 4. Evaluating the Model 5. Parameter Tuning 6. Making Predictions Let me know when is comes to build a ML model what do you see the major challenge? or any other steps would you like add to achieve more accuracy? Happy Learning and Sharing! Until we meet, happy leading, and let's lead together. Stay safe. Bye for now. Source - https://www.google.com/ #SoLeadSaturday Podcast/Show https://www.netapp.com/artificial-intelligence/what-is-machine-learning/ https://www.sas.com/en_us/insights/analytics/machine-learning.html https://quantilus.com/why-is-machine-learning-important-and-how-will-it-impact-business/ https://www.simplilearn.com/tutorials/machine-learning-tutorial/machine-learning-steps https://developers.google.com/machine-learning/guides/rules-of-ml https://neptune.ai/blog/improving-machine-learning-deep-learning-models Find me on - YoutTube - https://bit.ly/3dA0Qko #SoLeadSaturday Community Website - https://vaishalilambe.club/ Twitter - https://bit.ly/3Id2AMx LinkedIn - https://bit.ly/3tBe4Ft Instagram - @PassionPeoplePurpose Tiktok - @PassionPeoplePurpose Website - https://bit.ly/3GETAiS Facebook - https://bit.ly/3ry025c Apple Podcasts - https://apple.co/3KikjEi Google Podcasts - https://bit.ly/3fwzX0q Anchor - https://bit.ly/3rrij3R Spotify - https://spoti.fi/3Fw1tG4 Breaker - https://bit.ly/3Ib6ZQf Overcast - https://bit.ly/33GATNe Pocket casts - https://bit.ly/3tuNmyu Radio Public - https://bit.ly/33sfHLc Castbox - https://bit.ly/3KdUpl7 Video Editor - Vaishali Lambe Thumbnail Designer - Vaishali Lambe

Deep Learning Indepth Tutorials In 5 Hours With Krish Naik

Please get all the materials and pdfs in the below link which is for free. https://courses.ineuron.ai/Deep-Learning-Community-Class Join our amazing Programs Currently in iNeuron there are 3 main program that are going on. Full Stack Data science program with job guaranteed which started from May 6th(Price : 15k+ gst)-Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Science-Bootcamp Full Stack Data Analytics with placement assistance starting from June 18th(Price: 4000rs inr including gst)- Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Analytics Tech Neuron with 210+ courses (price: 7080 including gst for 2 years subscription) https://courses.ineuron.ai/neurons/Tech-Neuron From my side you can avail additional 10% discount by using coupon code Krish10 Or Sudhanshu10. Don't miss this opportunity grab it before it is too late. Happy Learning!! Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06

DeepMind’s New AI Thinks It Is A Genius! 🤖

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/08gdxK2 📝 The paper "DeepMind Gopher - Scaling Language Models: Methods, Analysis & Insights from Training Gopher" is available here: https://ift.tt/38zMbnl https://ift.tt/fB7UIry ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/jKhG0Ey - 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 Balfanz, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Ivo Galic, Jace O'Brien, Jack Lukic, Javier Bustamante, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Michael Tedder, Nevin Spoljaric, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Ted Johnson, 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/jKhG0Ey Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/pu0EWqd Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/WArbnvg

Deep Learning Indepth Tutorials In 5 Hours With Krish Naik

Please get all the materials and pdfs in the below link which is for free. https://courses.ineuron.ai/Deep-Learning-Community-Class Join our amazing Programs Currently in iNeuron there are 3 main program that are going on. Full Stack Data science program with job guaranteed which started from May 6th(Price : 15k+ gst)-Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Science-Bootcamp Full Stack Data Analytics with placement assistance starting from June 18th(Price: 4000rs inr including gst)- Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Analytics Tech Neuron with 210+ courses (price: 7080 including gst for 2 years subscription) https://courses.ineuron.ai/neurons/Tech-Neuron From my side you can avail additional 10% discount by using coupon code Krish10 Or Sudhanshu10. Don't miss this opportunity grab it before it is too late. Happy Learning!! Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06

machine learning about your future #short #easy

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TensorFlow 20 Complete Course Python Neural Networks for Beginners Tutorial

Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence. Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning. Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems. ⭐️ Google Colaboratory Notebooks ⭐️ 📕 Module 2: Introduction to TensorFlow - 📗 Module 3: Core Learning Algorithms - 📘 Module 4: Neural Networks with TensorFlow - 📙 Module 5: Deep Computer Vision - 📔 Module 6: Natural Language Processing with RNNs - 📒 Module 7: Reinforcement Learning - ⭐️ Course Contents ⭐️ ⌨️ (00:03:25) Module 1: Machine Learning Fundamentals ⌨️ (00:30:08) Module 2: Introduction to TensorFlow ⌨️ (01:00:00) Module 3: Core Learning Algorithms ⌨️ (02:45:39) Module 4: Neural Networks with TensorFlow ⌨️ (03:43:10) Module 5: Deep Computer Vision - Convolutional Neural Networks ⌨️ (04:40:44) Module 6: Natural Language Processing with RNNs ⌨️ (06:08:00) Module 7: Reinforcement Learning with Q-Learning ⌨️ (06:48:24) Module 8: Conclusion and Next Steps ⭐️ About the Author ⭐️ The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below: 🔗 YouTube: 🔗 LinkedIn: -- Learn to code for free and get a developer job: Read hundreds of articles on programming: And subscribe for new videos on technology every day: source:

machine learning about your future #short #easy

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Impress your girlfriend using python

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Day 4-Practical ANN Impementation| Live Deep Learning Community Session

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AI & Analytics Engine Tutorial | Regression ML problem

In this tutorial, we show you how you can use the AI & Analytics Engine, a no-code AutoML platform, to solve a regression ML problem type. In this video, we take you from data to predictions. It's simple! Website: https://www.pi.exchange​ Free trial: https://aiaengine.com/auth/register Music: Scott Holmes, Think Big.

Kortical - An AI Cloud Platform to support the full machine learning life cycle

An introductory video for the Kortical Machine Learning platform. Making data scientist's lives easier since 2016. It's created by data scientists for data scientists and while this video shows a lot of UI, there is a full suite of code tools to drive everything too. It's used by lots of fortune 500s including Deloitte, Charlotte Tilbury, Santander, Hyundai. If you've seen this teaser but want to see more, check out the full end to end, raw data to live Machine Learning Bookkeeping App video https://youtu.be/hPBMvCO9YN4 To try for yourself, send us a message here https://bit.ly/3kDytEp

Linear regression single variable Tutorial

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How to use naive bayes in machine learning (part 1)

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Author Interview: SayCan - Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

#saycan #robots #ai This is an interview with the authors Brian Ichter, Karol Hausman, and Fei Xia. Original Paper Review Video: https://youtu.be/Ru23eWAQ6_E Large Language Models are excellent at generating plausible plans in response to real-world problems, but without interacting with the environment, they have no abilities to estimate which of these plans are feasible or appropriate. SayCan combines the semantic capabilities of language models with a bank of low-level skills, which are available to the agent as individual policies to execute. SayCan automatically finds the best policy to execute by considering a trade-off between the policy's ability to progress towards the goal, given by the language model, and the policy's probability of executing successfully, given by the respective value function. The result is a system that can generate and execute long-horizon action sequences in the real world to fulfil complex tasks. OUTLINE: 0:00 - Introduction & Setup 3:40 - Acquiring atomic low-level skills 7:45 - How does the language model come in? 11:45 - Why are you scoring instead of generating? 15:20 - How do you deal with ambiguity in language? 20:00 - The whole system is modular 22:15 - Going over the full algorithm 23:20 - What if an action fails? 24:30 - Debunking a marketing video :) 27:25 - Experimental Results 32:50 - The insane scale of data collection 40:15 - How do you go about large-scale projects? 43:20 - Where did things go wrong? 45:15 - Where do we go from here? 52:00 - What is the largest unsolved problem in this? 53:35 - Thoughts on the Tesla Bot 55:00 - Final thoughts Paper: https://ift.tt/HGetosQ Website: https://ift.tt/RIrf48J Abstract: Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at this https URL Authors: Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan 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/Fh9K5RX BitChute: https://ift.tt/dlhPLgz LinkedIn: https://ift.tt/XdSrB6I BiliBili: https://ift.tt/ypvC0JH 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/Ta7RGEF Patreon: https://ift.tt/xMLVEZI Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

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