Tutorial on Adversarial Deep Learning for Cyber Security

Speaker : Dr. Aneesh S Chivukula

Speed up your coding with AI - JavaScript Tutorial for beginners 2022

#shorts #howto #coding #javascript #tutorial #ai #artificialintelligence #machine #machinelearning javascript tutorial for beginners machine learning

ML5G Challenge tutorial #3: Machine Learning model building and training | AI/ML IN 5G CHALLENGE

As part of the Machine Learning in 5G Challenge, this tutorial is open to participants or students who are new to machine learning and their application to communication networks. The tutorial will The session will cover the general introduction to Machine Learning Algorithms. Some concepts to be introduced includes: types of machine learning, model building, and model training, followed by a Jupiter notebook practice covering these topics, and a Q&A session at the end. 🎙 Speakers: Thomas Basikolo‬ Young Expert - AI/ML International Telecommunication Union (ITU) 🎙 Moderators: Miya Nishio Student University of Tokyo #ML5G #5GNetworks Join the Neural Network! 👉https://aiforgood.itu.int/neural-network/ The AI for Good networking community platform powered by AI. Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI. 🔴 Watch the latest #AIforGood videos! https://www.youtube.com/c/AIforGood/videos Explore more #AIforGood content: 1️⃣ AI for Good Top Hits https://www.youtube.com/playlist?list=PLQqkkIwS_4kXXQWhP4vSr0Rtl13t2Ud2w 2️⃣ AI for Good Webinars https://www.youtube.com/playlist?list=PLQqkkIwS_4kUwDVioxixBf8OYlGAS_9_z 3️⃣ AI for Good Keynotes https://www.youtube.com/playlist?list=PLQqkkIwS_4kUmK6zjiuWWE4fImrbGBWRF 📩 Stay updated and join our weekly AI for Good newsletter: http://eepurl.com/gI2kJ5 📅 Discover what's next on our programme! https://aiforgood.itu.int/programme/ 🗞Check out the latest AI for Good news: https://aiforgood.itu.int/newsroom/ 📱Explore the AI for Good blog: https://aiforgood.itu.int/ai-for-good-blog/ 🌎 Connect on our social media: Website: https://aiforgood.itu.int/ Twitter: https://twitter.com/AIforGood LinkedIn Page: https://www.linkedin.com/company/26511907 LinkedIn Group: https://www.linkedin.com/groups/8567748 Instagram: https://www.instagram.com/aiforgood Facebook: https://www.facebook.com/AIforGood What is AI for Good? We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets. More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals. AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland. Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

ML5G Challenge tutorial #3: Machine Learning model building and training | AI/ML IN 5G CHALLENGE

As part of the Machine Learning in 5G Challenge, this tutorial is open to participants or students who are new to machine learning and their application to communication networks. The tutorial will The session will cover the general introduction to Machine Learning Algorithms. Some concepts to be introduced includes: types of machine learning, model building, and model training, followed by a Jupiter notebook practice covering these topics, and a Q&A session at the end. 🎙 Speakers: Thomas Basikolo‬ Young Expert - AI/ML International Telecommunication Union (ITU) 🎙 Moderators: Miya Nishio Student University of Tokyo #ML5G #5GNetworks Join the Neural Network! 👉https://aiforgood.itu.int/neural-network/ The AI for Good networking community platform powered by AI. Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI. 🔴 Watch the latest #AIforGood videos! https://www.youtube.com/c/AIforGood/videos Explore more #AIforGood content: 1️⃣ AI for Good Top Hits https://www.youtube.com/playlist?list=PLQqkkIwS_4kXXQWhP4vSr0Rtl13t2Ud2w 2️⃣ AI for Good Webinars https://www.youtube.com/playlist?list=PLQqkkIwS_4kUwDVioxixBf8OYlGAS_9_z 3️⃣ AI for Good Keynotes https://www.youtube.com/playlist?list=PLQqkkIwS_4kUmK6zjiuWWE4fImrbGBWRF 📩 Stay updated and join our weekly AI for Good newsletter: http://eepurl.com/gI2kJ5 📅 Discover what's next on our programme! https://aiforgood.itu.int/programme/ 🗞Check out the latest AI for Good news: https://aiforgood.itu.int/newsroom/ 📱Explore the AI for Good blog: https://aiforgood.itu.int/ai-for-good-blog/ 🌎 Connect on our social media: Website: https://aiforgood.itu.int/ Twitter: https://twitter.com/AIforGood LinkedIn Page: https://www.linkedin.com/company/26511907 LinkedIn Group: https://www.linkedin.com/groups/8567748 Instagram: https://www.instagram.com/aiforgood Facebook: https://www.facebook.com/AIforGood What is AI for Good? We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets. More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals. AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland. Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

Machine Learning Tutorial for Beginners | Applied Machine Learning Foundations

Welcome to my Channel...! In this video we are going to see the basics of Applied Machine Learning . These are the fundamentals of Applied Machine Learning and essential trainings. we will see more and more in upcoming videos. For any queries drop a mail at contact.missgoo@gmail.com Share your thoughts about this video in the comment section and if you have any doubts post it in comment section. BluePrism Playlist:- https://youtube.com/playlist?list=PLWMB5IYAuU6fwXy7lFh627J9buCMnPgRU Thank You...! →→→→→Visit Our Channel For More Videos←←←←← 🏹LIKE 🏹SHARE 🏹SUBSCRIBE Where There is a Will There is a Way 💘 //Chapters and time splits 00:00:00-00:01:46 Leveraging machine learning 00:01:47-00:02:52 What you should know 00:02:53-00:03:36 What tools you need 00:03:37-00:07:37 What is machine learning? 00:07:38-00:12:39 What kind of problems can this help you solve? 00:12:40-00:18:28 Why Python? 00:18:29-00:22:17 Machine learning vs Deep learning vs Artificial intelligence 00:22:18-00:25:16 Demos of machine learning in real life 00:25:17-00:31:20 Common challenges 00:31:21-00:34:49 Why do we need to explore and clean our data? 00:34:50-00:43:35 Exploring continuous features 00:43:36-00:51:10 Plotting continuous features 00:51:11-00:56:54 Continuous data cleaning 00:56:55-01:02:58 Exploring categorical features 01:02:59-01:09:18 Plotting categorical features 01:09:19-01:13:51 Categorical data cleaning 01:13:52-01:19:45 Why do we split up our data? 01:19:46-01:24:52 Split data for train/validation/test set 01:24:53-01:30:55 What is cross-validation? 01:30:56-01:35:28 Establish an evaluation framework 01:35:29-01:40:28 Bias/Variance tradeoff 01:40:29-01:42:54 What is underfitting? 01:42:55-01:45:41 What is overfitting? 01:45:42-01:48:57 Finding the optimal tradeoff 01:48:58-01:55:19 Hyperparameter tuning 01:55:20-01:57:50 Regularization 01:57:51-01:59:38 Overview of the process 01:59:39-02:04:42 Clean continuous features 02:04:43-02:09:00 Clean categorical features 02:09:01-02:12:48 Split data into train/validation/test set 02:12:49-02:18:09 Fit a basic model using cross-validation 02:18:10-02:24:43 Tune hyperparameters 02:24:44-02:31:26 Evaluate results on validation set 02:31:27-02:35:56 Final model selection and evaluation on test set 02:35:57-02:37:23 Next steps

Pixeltests Community session - Blockchain and AI

3 secrets to securing a high paying Blockchain and Artificial Intelligence job with resume mastery! Pixeltests is an Edtech aimed at upskilling professionals into Web 3.0, Blockchain, Artificial Intelligence, Machine Learning, etc. This is the first lecture for the paid community. We meet weekly live for Q&A, doubts! If you want to know more about the community, please join free live 2 hour webinar! Blockchain: https://bit.ly/bl_live4 Artificial Intelligence: https://bit.ly/ai_live3 pixeltests course,pixeltests students,pixeltests webinar,blockchain course,web 3.0 course review,machine learning course,machine learning course review,data science course,data science course review,data science placements,resume workshop,cv workshop,blockchain,curriculum vitae,cv kaise banaye,resume kaise banaye,resume format,resume writing,resume in ms word,resume for freshers,resume writing in english,machine learning cv,machine learning cv skills Facebook: https://www.facebook.com/fbpixeltests/ LinkedIn: https://www.linkedin.com/company/pixeltests/ Instagram: https://www.instagram.com/pixeltests1/ Twitter: https://twitter.com/thePixeltests

AI Learning to Play Breakout using Reinforcement Learning || Pygame

Machine Learning Tutorial for Beginners | Applied Machine Learning Foundations

Welcome to my Channel...! In this video we are going to see the basics of Applied Machine Learning . These are the fundamentals of Applied Machine Learning and essential trainings. we will see more and more in upcoming videos. For any queries drop a mail at contact.missgoo@gmail.com Share your thoughts about this video in the comment section and if you have any doubts post it in comment section. BluePrism Playlist:- https://youtube.com/playlist?list=PLWMB5IYAuU6fwXy7lFh627J9buCMnPgRU Thank You...! →→→→→Visit Our Channel For More Videos←←←←← 🏹LIKE 🏹SHARE 🏹SUBSCRIBE Where There is a Will There is a Way 💘 //Chapters and time splits 00:00:00-00:01:46 Leveraging machine learning 00:01:47-00:02:52 What you should know 00:02:53-00:03:36 What tools you need 00:03:37-00:07:37 What is machine learning? 00:07:38-00:12:39 What kind of problems can this help you solve? 00:12:40-00:18:28 Why Python? 00:18:29-00:22:17 Machine learning vs Deep learning vs Artificial intelligence 00:22:18-00:25:16 Demos of machine learning in real life 00:25:17-00:31:20 Common challenges 00:31:21-00:34:49 Why do we need to explore and clean our data? 00:34:50-00:43:35 Exploring continuous features 00:43:36-00:51:10 Plotting continuous features 00:51:11-00:56:54 Continuous data cleaning 00:56:55-01:02:58 Exploring categorical features 01:02:59-01:09:18 Plotting categorical features 01:09:19-01:13:51 Categorical data cleaning 01:13:52-01:19:45 Why do we split up our data? 01:19:46-01:24:52 Split data for train/validation/test set 01:24:53-01:30:55 What is cross-validation? 01:30:56-01:35:28 Establish an evaluation framework 01:35:29-01:40:28 Bias/Variance tradeoff 01:40:29-01:42:54 What is underfitting? 01:42:55-01:45:41 What is overfitting? 01:45:42-01:48:57 Finding the optimal tradeoff 01:48:58-01:55:19 Hyperparameter tuning 01:55:20-01:57:50 Regularization 01:57:51-01:59:38 Overview of the process 01:59:39-02:04:42 Clean continuous features 02:04:43-02:09:00 Clean categorical features 02:09:01-02:12:48 Split data into train/validation/test set 02:12:49-02:18:09 Fit a basic model using cross-validation 02:18:10-02:24:43 Tune hyperparameters 02:24:44-02:31:26 Evaluate results on validation set 02:31:27-02:35:56 Final model selection and evaluation on test set 02:35:57-02:37:23 Next steps

Intel’s New AI: Amazing Ray Tracing Results! ☀️

❤️ Check out Weights & Biases and say hi in their community forum here: https://ift.tt/Au4U0dI 📝 The paper "Temporally Stable Real-Time Joint Neural Denoising and Supersampling" is available here: https://ift.tt/avEpj8I 📝 Our earlier paper with the spheres scene that took 3 weeks: https://ift.tt/2XvahjU ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/toZnF2g - 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, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique Warner, Matthew Allen Fisher, Matthew Valle, 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/toZnF2g Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/ES97V1U Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/EbVsStf

ML5G Challenge tutorial #3: Machine Learning model building and training | AI/ML IN 5G CHALLENGE

As part of the Machine Learning in 5G Challenge, this tutorial is open to participants or students who are new to machine learning and their application to communication networks. The tutorial will The session will cover the general introduction to Machine Learning Algorithms. Some concepts to be introduced includes: types of machine learning, model building, and model training, followed by a Jupiter notebook practice covering these topics, and a Q&A session at the end. 🎙 Speakers: Thomas Basikolo‬ Young Expert - AI/ML International Telecommunication Union (ITU) 🎙 Moderators: Miya Nishio Student University of Tokyo #ML5G #5GNetworks Join the Neural Network! 👉https://aiforgood.itu.int/neural-network/ The AI for Good networking community platform powered by AI. Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI. 🔴 Watch the latest #AIforGood videos! https://www.youtube.com/c/AIforGood/videos Explore more #AIforGood content: 1️⃣ AI for Good Top Hits https://www.youtube.com/playlist?list=PLQqkkIwS_4kXXQWhP4vSr0Rtl13t2Ud2w 2️⃣ AI for Good Webinars https://www.youtube.com/playlist?list=PLQqkkIwS_4kUwDVioxixBf8OYlGAS_9_z 3️⃣ AI for Good Keynotes https://www.youtube.com/playlist?list=PLQqkkIwS_4kUmK6zjiuWWE4fImrbGBWRF 📩 Stay updated and join our weekly AI for Good newsletter: http://eepurl.com/gI2kJ5 📅 Discover what's next on our programme! https://aiforgood.itu.int/programme/ 🗞Check out the latest AI for Good news: https://aiforgood.itu.int/newsroom/ 📱Explore the AI for Good blog: https://aiforgood.itu.int/ai-for-good-blog/ 🌎 Connect on our social media: Website: https://aiforgood.itu.int/ Twitter: https://twitter.com/AIforGood LinkedIn Page: https://www.linkedin.com/company/26511907 LinkedIn Group: https://www.linkedin.com/groups/8567748 Instagram: https://www.instagram.com/aiforgood Facebook: https://www.facebook.com/AIforGood What is AI for Good? We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets. More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals. AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland. Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

tinyML Auto ML Deep Dive Tutorial with imagimob - Building Production-ready Models using Imagimob AI

tinyML Auto ML Tutorial with imagimob Building Production-ready Models using Imagimob AI Imagimob AI is a development platform designed to streamline the process of creating production-ready ANN models for time-series data to be run on devices with constrained resources. It’s built on Imagimob’s experience of enabling AI and ML functionality for different customers and products. The same tool we use ourselves for building these models we have opened up to the public, everything we have found to be useful we believe the customer will as well. Imagimob AI is low code but gives the user all the tools they need to go from a sensor connection to a model that is simple to integrate and completely ready with the associated pre-processing.

Machine Learning with Python Tutorial for Beginners

Welcome to my Channel...! In this video we are going to see the basic concepts of Machine Learning with Python . These are the fundamentals of Machine Learning with Python and essential trainings. we will see more and more in upcoming videos. For any queries drop a mail at contact.missgoo@gmail.com Share your thoughts about this video in the comment section and if you have any doubts post it in comment section. BluePrism Playlist:- https://youtube.com/playlist?list=PLWMB5IYAuU6fwXy7lFh627J9buCMnPgRU Thank You...! →→→→→Visit Our Channel For More Videos←←←←← 🏹LIKE 🏹SHARE 🏹SUBSCRIBE Where There is a Will There is a Way 💘 //Chapters and time splits 00:00:00-00:00:50 Machine learning in our world 00:00:51-00:01:35 What you should know 00:01:36-00:02:37 The tools you need 00:02:38-00:06:36 What is machine learning? 00:06:37-00:10:31 What is not machine learning? 00:10:32-00:13:10 What is unsupervised learning? 00:13:11-00:17:15 What is supervised learning? 00:17:16-00:23:21 What is reinforcement learning? 00:23:22-00:28:34 What are the steps to machine learning? 00:28:35-00:32:22 Things to consider when collecting data 00:32:23-00:37:25 How to import data in Python 00:37:26-00:40:58 Describe your data 00:40:59-00:46:58 How to summarize data in Python 00:46:59-00:50:40 Visualize your data 00:50:41-00:57:03 How to visualize data in Python 00:57:04-01:00:45 Common data quality issues 01:00:46-01:08:19 How to resolve missing data in Python 01:08:20-01:12:58 Normalizing your data 01:12:59-01:17:36 How to normalize data in Python 01:17:37-01:24:23 Sampling your data 01:24:24-01:30:58 How to sample data in Python 01:30:59-01:34:22 Reducing the dimensionality of your data 01:34:23-01:37:37 Classification vs regression problems 01:37:38-01:43:32 How to build a machine learning model in Python 01:43:33-01:44:57 Next steps with applied machine learning

Guide to Build AI Product Idea MVP with Hugging Face Tech Stack

In this Machine Learning Tutorial, We'll introduce a Guide to Build your AI Product Idea MVP in less than 24 hours with Hugging Face Tech Stack. What do you need for an AI Startup MVP (Minimum Viable Product) ? 💡. Idea 🤖. Solution aka ML Model 💻. GUI ⚙️. App Hosting 🚀. Option to Scale An MVP is one of the best ways to validate a product idea and in fact if you want to gain interest from users or Investors. We'll learn how to use HuggingFace.co to cover all these topics. https://huggingface.co/

Watch NVIDIA’s AI Teach This Human To Run! 🏃‍♂️

❤️ Check out Cohere and sign up for free today: https://ift.tt/kLo50Sm 📝 The paper "Accelerated Policy Learning with Parallel Differentiable Simulation" is available here: https://ift.tt/FVyzdmA ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/bX1U2Hv - 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, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique Warner, 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/bX1U2Hv Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/5NYQTKo Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/uElxATX #nvidia

Here is out latest tutorial on YOLOv6. #deeplearning #ai #computervision #machinelearning #yolo

Wow, A Simulation That Looks Like Reality! 🤯

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/IRU0YTB 📝 My paper "The flow from simulation to reality" with clickable citations is available here: https://ift.tt/ximl8yh 📝 Read it for free here! https://rdcu.be/cWPfD ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/UovATL6 - 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, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique Warner, 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/UovATL6 Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/Sae308T Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/7fV5gzI

Python Tutorial Day -1 #python #AI #machinelearning #Datascince #anaconda #new #coding

Python Tutorial Day -1 #python #AI #machinelearning #Datascince #anaconda #new #coding From today onwords am sarting 🆕 #new series .will cover all the techniqes in #python which are requesrs for #Datascince field.it's easy to understnd,from ground zero onwords people can watch it and they can do the coding as well. if still people have any kind of issue or doubt then kindly write me at : 💭💭💭letsplaywithdata.007@gmail.com ✔ am Happy to help you ❤ #anconda dwonload link : https://www.anaconda.com/products/distribution This stream is created with #PRISMLiveStudio

Tutorial: Beyond Neural Search: Hands-on Building Cross-Modal/Multi-Moda... Han Xiao & Sami Jaghouar

Tutorial: Beyond Neural Search: Hands-on Building Cross-Modal/Multi-Modal Solution with Jina AI - Han Xiao & Sami Jaghouar, Jina AI Neural search is using deep learning to search unstructured data, which has been developed rapidly over the last 2 years. With opensource framework like Jina (https://github.com/jina-ai/jina), searching cross-modal/multi-modal data via deep neural networks becomes extremely straightforward. DALL·E, a powerful image-to-text generator released by OpenAI in 2021 further boosts the popularity of multimodal applications. We now see thousands of astonishing artwork made by DALL·E every day. How all these new technologies will change our way of comprehending data? Most importantly, how can developers easily build solutions & applications leveraging those technologies? This tutorial will answer these two questions. This tutorial is bi-partite. In part 1, Dr. Han Xiao will introduce the recent advances of neural search and multi-modal intelligence. He will break down the design principle of Jina ecosystem. In part 2 (bring your laptop), Han will guide step by step to build DALL·E Flow: a Human-in-the-Loop workflow for creating HD images from text. He will demonstrate how Jina unlocks multi-modal/cross-modal capability in your solution & applications. This is a great chance to get hands dirty with Jina and DocArray's Pythonic API and to embrace the charm of generative arts.

This is a game changer! (AlphaTensor by DeepMind explained)

#alphatensor #deepmind #ai Matrix multiplication is the most used mathematical operation in all of science and engineering. Speeding this up has massive consequences. Thus, over the years, this operation has become more and more optimized. A fascinating discovery was made when it was shown that one actually needs less than N^3 multiplication operations to multiply to NxN matrices. DeepMind goes a step further and creates AlphaTensor, a Deep Reinforcement Learning algorithm that plays a single-player game, TensorGame, in order to find even more optimized algorithms for matrix multiplication. And it turns out, there exists a plethora of undiscovered matrix multiplication algorithms, which not only will make everything from computers to smart toasters faster, but also bring new insights into fundamental math and complexity theory. Sponsor: Assembly AI Link: https://ift.tt/MCAg3sG OUTLINE: 0:00 - Intro 1:50 - Sponsor: Assembly AI (link in description) 3:25 - What even is Matrix Multiplication? 6:10 - A very astounding fact 8:45 - Trading multiplications for additions 12:35 - Matrix Multiplication as a Tensor 17:30 - Tensor Decompositions 20:30 - A formal way of finding multiplication algorithms 31:00 - How to formulate this as a game? 39:30 - A brief primer on AlphaZero / MCTS 45:40 - The Results 48:15 - Optimizing for different hardware 52:40 - Expanding fundamental math 53:45 - Summary & Final Comments Paper: https://ift.tt/g4irfy6 Title: Discovering faster matrix multiplication algorithms with reinforcement learning Abstract: Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero1 for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor’s ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria. Authors: Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis & Pushmeet Kohli Links: Homepage: https://ykilcher.com Merch: https://ift.tt/6utQgH3 YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/a1Mem9b LinkedIn: https://ift.tt/AfyXs1a 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/s34IxXA Patreon: https://ift.tt/DiQC4mj Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

ML5G Challenge tutorial: introduction to communication networks | AI/ML IN 5G CHALLENGE

As part of the Machine Learning in 5G Challenge, this mentoring session is open to participants or students who are new to networking or telecommunications. The tutorial will introduce some common concepts in a simplified way so that you can become familiar with basic and common terms used. 🎙 Speakers: Thomas Basikolo‬, Young Expert - AI/ML, International Telecommunication Union (ITU) Vishnu Ram OV, Independent Research Consultant, International Telecommunication Union (ITU) #ML5G 5GNetworks Join the Neural Network! 👉https://aiforgood.itu.int/neural-network/ The AI for Good networking community platform powered by AI. Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI. 🔴 Watch the latest #AIforGood videos! https://www.youtube.com/c/AIforGood/videos Explore more #AIforGood content: 1️⃣ AI for Good Top Hits https://www.youtube.com/playlist?list=PLQqkkIwS_4kXXQWhP4vSr0Rtl13t2Ud2w 2️⃣ AI for Good Webinars https://www.youtube.com/playlist?list=PLQqkkIwS_4kUwDVioxixBf8OYlGAS_9_z 3️⃣ AI for Good Keynotes https://www.youtube.com/playlist?list=PLQqkkIwS_4kUmK6zjiuWWE4fImrbGBWRF 📩 Stay updated and join our weekly AI for Good newsletter: http://eepurl.com/gI2kJ5 📅 Discover what's next on our programme! https://aiforgood.itu.int/programme/ 🗞Check out the latest AI for Good news: https://aiforgood.itu.int/newsroom/ 📱Explore the AI for Good blog: https://aiforgood.itu.int/ai-for-good-blog/ 🌎 Connect on our social media: Website: https://aiforgood.itu.int/ Twitter: https://twitter.com/AIforGood LinkedIn Page: https://www.linkedin.com/company/26511907 LinkedIn Group: https://www.linkedin.com/groups/8567748 Instagram: https://www.instagram.com/aiforgood Facebook: https://www.facebook.com/AIforGood What is AI for Good? We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets. More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals. AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland. Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

Try Using This Technique to Make Better AI Art

This channel is about Bridging the gap between humans and machines, If you like tech & AI, Join our discord: https://discord.gg/EzAzRJey If you want to learn my prompts, they will be in the discord. #shorts https://www.factoryofthesol.com/ This is a video on how to use and compare mid journey and DALLE2. If you want to learn how to use AI check out my other videos or send me a message on discord. midjourney,how to use ai,text to image ai,text to image generation,dall e text to image,dall e 2,ai art generator,dall-e 2 examples,dall e,artificial intelligence,ai,ai generated art,openai,mid journey,guide to midjourney,ai art,animating with ai,computer makes art,dall-e 2,concept art,machine learning,artificial intelligence tutorial,tensorflow,artificial intelligence for beginners,machine learning tutorial,artificial intelligence course

Google Supercharged Stable Diffusion! 🧑‍🎨

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/b73gQUC ❤️ Their mentioned post is available here: https://ift.tt/taQ4woN 📝 The paper "Prompt-to-Prompt Image Editing with Cross Attention Control" is available here: https://ift.tt/4zRFi69 Unofficial open source implementation: https://ift.tt/fPZx9CR ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/w0gZqeP - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Stable Diffusion frame interpolation: https://twitter.com/xsteenbrugge/status/1558508866463219712 Full video of interpolation: https://www.youtube.com/watch?v=Bo3VZCjDhGI 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique Warner, 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/w0gZqeP Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/m3HjPwO Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/lXtwDJk

ML5G Challenge tutorial: introduction to communication networks | AI/ML IN 5G CHALLENGE

As part of the Machine Learning in 5G Challenge, this mentoring session is open to participants or students who are new to networking or telecommunications. The tutorial will introduce some common concepts in a simplified way so that you can become familiar with basic and common terms used. 🎙 Speakers: Thomas Basikolo‬, Young Expert - AI/ML, International Telecommunication Union (ITU) Vishnu Ram OV, Independent Research Consultant, International Telecommunication Union (ITU) #ML5G 5GNetworks Join the Neural Network! 👉https://aiforgood.itu.int/neural-network/ The AI for Good networking community platform powered by AI. Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI. 🔴 Watch the latest #AIforGood videos! https://www.youtube.com/c/AIforGood/videos Explore more #AIforGood content: 1️⃣ AI for Good Top Hits https://www.youtube.com/playlist?list=PLQqkkIwS_4kXXQWhP4vSr0Rtl13t2Ud2w 2️⃣ AI for Good Webinars https://www.youtube.com/playlist?list=PLQqkkIwS_4kUwDVioxixBf8OYlGAS_9_z 3️⃣ AI for Good Keynotes https://www.youtube.com/playlist?list=PLQqkkIwS_4kUmK6zjiuWWE4fImrbGBWRF 📩 Stay updated and join our weekly AI for Good newsletter: http://eepurl.com/gI2kJ5 📅 Discover what's next on our programme! https://aiforgood.itu.int/programme/ 🗞Check out the latest AI for Good news: https://aiforgood.itu.int/newsroom/ 📱Explore the AI for Good blog: https://aiforgood.itu.int/ai-for-good-blog/ 🌎 Connect on our social media: Website: https://aiforgood.itu.int/ Twitter: https://twitter.com/AIforGood LinkedIn Page: https://www.linkedin.com/company/26511907 LinkedIn Group: https://www.linkedin.com/groups/8567748 Instagram: https://www.instagram.com/aiforgood Facebook: https://www.facebook.com/AIforGood What is AI for Good? We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets. More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals. AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland. Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

Try Using This Technique to Make Better AI Art

This channel is about Bridging the gap between humans and machines, If you like tech & AI, Join our discord: https://discord.gg/EzAzRJey If you want to learn my prompts, they will be in the discord. #shorts https://www.factoryofthesol.com/ This is a video on how to use and compare mid journey and DALLE2. If you want to learn how to use AI check out my other videos or send me a message on discord. midjourney,how to use ai,text to image ai,text to image generation,dall e text to image,dall e 2,ai art generator,dall-e 2 examples,dall e,artificial intelligence,ai,ai generated art,openai,mid journey,guide to midjourney,ai art,animating with ai,computer makes art,dall-e 2,concept art,machine learning,artificial intelligence tutorial,tensorflow,artificial intelligence for beginners,machine learning tutorial,artificial intelligence course

Python Tutorial in Tamil | Beginners to Advanced | 2 week python

ITET is a cutting-edge Online Education platform, headquartered in Chennai. We provide professional courses in python, IoT, Artificial Intelligence, Machine Learning and Data science employing our own high-level researcher as mentor. Learn Coding Free, Python Programming Tamil, Machine Learning in Tamil, IoT in Tamil, Data science in Tamil, artificial intelligence in tamil For more information, visit https://itexperttraining.com//

[ML News] OpenAI's Whisper | Meta Reads Brain Waves | AI Wins Art Fair, Annoys Humans

#mlnews #openai #ai Everything important going on in the ML world right here! Sponsor: Paperspace https://ift.tt/sxwzaW9 OUTLINE: 0:00 - Introduction 0:20 - Whisper: Open-Source Speech Transcription 6:30 - Sponsor: Paperspace 9:30 - Meta: How the brain hears audio 11:25 - PyTorch moves to Linux Foundation 12:15 - French Government uses AI to find unlicensed swimming pools 13:35 - AlphaFold extends database 14:10 - John Carmack raises 20M to build AGI0729970510422016 16:10 - Cerebras achieves model size record 17:40 - Andrej Karpathy on YouTube 18:35 - ColabPro changes pricing 19:15 - Huggingface runs evaluation on the hub 20:35 - AI wins art fair 22:50 - PaLI: Multilingual Language-Image Learning 23:40 - Operationalizing Machine Learning: An Interview Study 24:35 - LAION OpenCLIP: New Models 25:10 - BlenderBot 3 175B Released 25:45 - OWL-ViT on the Hub 26:10 - GLM-130B 26:35 - Ernie-ViLG 27:10 - Digitizing Smell using Molecular Maps 28:00 - AlexaTM 20B 29:00 - Audio-LM 29:45 - Useful Things 37:20 - Raycasting in JAX 38:00 - GPT-3 Prompt Injection 39:20 - GPT-3 plus Python 40:45 - Game Emulation via DNN References here (external bc too long for YT): https://ift.tt/Kf1yIOQ Links: Homepage: https://ykilcher.com Merch: https://ift.tt/lfGN7pc YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/87iynak LinkedIn: https://ift.tt/9QLZapb 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/pMFiCgU Patreon: https://ift.tt/F5yvxhO Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Python Tutorial in Tamil with Real-Time Examples| 2 week python | part-8

ITET is a cutting-edge Online Education platform, headquartered in Chennai. We provide professional courses in python, IoT, Artificial Intelligence, Machine Learning and Data science employing our own high-level researcher as mentor. Learn Coding Free, Python Programming Tamil, Machine Learning in Tamil, IoT in Tamil, Data science in Tamil, artificial intelligence in tamil For more information, visit https://itexperttraining.com//