[ML News] This AI completes Wikipedia! Meta AI Sphere | Google Minerva | GPT-3 writes a paper

#mlnews #ai #minerva This episode is all about models that reason. OUTLINE: 0:00 - Intro 0:35 - Meta AI learns Wikipedia citations 5:25 - Google's Minerva solves math problems by reading papers 9:10 - GPT-3 writes a paper on itself 13:35 - Jürgen Schmidhuber prompts LeCun for missing citations References: Meta AI learns Wikipedia citations https://ift.tt/qckEYCP https://ift.tt/f76ObYt https://ift.tt/t290jpR https://ift.tt/17rcAyD https://ift.tt/4tuwLFD https://ift.tt/sgSV37J Google's Minerva solves math problems by reading papers https://ift.tt/XAohWJa https://ift.tt/3t6LpqP GPT-3 writes a paper on itself https://ift.tt/XU8oPbK https://ift.tt/TzpeHIY https://ift.tt/wUgtrpW Jürgen Schmidhuber prompts LeCun for missing citations https://ift.tt/Qz8vVq9 Links: Homepage: https://ykilcher.com Merch: https://ift.tt/jlMb6OX YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/OhRpMDH LinkedIn: https://ift.tt/3CjIezD 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/FGb8K5o Patreon: https://ift.tt/Uq1NZHG Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

YOLOv7 Tutorial | Machine Learning Algorithms | Deep Learning

#shorts #short Course Playlists- Machine Learning & Data Science - Beginner to Professional Hands-on Python Course in Hindi: https://www.youtube.com/playlist?list=PLfP3JxW-T70Hh7j17_NLzjZ8CejSPx40V Deep Learning Project End to End in Hindi : https://www.youtube.com/playlist?list=PLfP3JxW-T70FfgI3BSRjjwgFvLOyufID1 Machine Learning Project in Hindi: https://www.youtube.com/playlist?list=PLfP3JxW-T70GzK_mU0oWYbnMhjWbVc2O5 Python NumPy Tutorial in Hindi: https://www.youtube.com/playlist?list=PLfP3JxW-T70FKkXT9VEeRChKvF4EUInWj Python Pandas Tutorial in Hindi https://www.youtube.com/playlist?list=PLfP3JxW-T70Gf4iJXPb0Yw5_-tDRCD6LB Python Matplotlib Tutorial in Hindi: https://www.youtube.com/playlist?list=PLfP3JxW-T70EfCmI71WF29Q1sDN8WMp4c Python Seaborn Tutorial in Hindi: https://www.youtube.com/playlist?list=PLfP3JxW-T70HaBYwsSDadlS3v2VeALgYh …………………………………………………………………………………………………………………… For more information: Contact Us: ========= -Website: https://www.indianaiproduction.com -Facebook: https://www.facebook.com/indianaiproduction -Instagram: https://www.instagram.com/indianaiproduction -Twitter: https://twitter.com/indianaipro -LinkedIn: https://www.linkedin.com/in/indianaiproduction/ -Telegram: https://t.me/IndianAIProduction ……………………………………………………………………………………………………………………

Data Science, Machine Learning, and AI Summer Course - Recurrent Neural Networks

A recurrent neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order. Recursive neural networks, sometimes abbreviated as RvNNs, have been successful, for instance, in learning sequence and tree structures in natural language processing, mainly phrase and sentence continuous representations based on word embedding. RvNNs have first been introduced to learn distributed representations of structure, such as logical terms. Models and general frameworks have been developed in further works since the 1990s. -------------------- For more information, please go to: 👉 Website: ---------------------------------------- https://wyn-associates.com/ 👉 YouTube Channel: ------------------------- https://www.youtube.com/channel/UCf4pY0OuNzQ2jwhIueBHzQQ 👉 Coffee Time with Mr. Yin! ☕: ------- https://www.youtube.com/playlist?list=PLE06V2Lg9GqPm7-pi4wU87q7MuGo16BsX 👉 Yin's Squawk Box 🐤: -------------------- https://www.youtube.com/playlist?list=PLE06V2Lg9GqP-fZSKQXK9qhwTTejR1ro2 👉 Yin's Py 🥧: ------------------------------------ https://www.youtube.com/playlist?list=PLE06V2Lg9GqMQvKgyQdTChFL3Oz_mhQt8 👉 Read AI with Me 📚: ---------------------- https://www.youtube.com/playlist?list=PLE06V2Lg9GqNelAqW03qOoeVpheo1vObW 👉 ML|DS|AI Summer Program: ---------- https://www.youtube.com/playlist?list=PLE06V2Lg9GqO5cGWqrab1ocP2kOVqa7ZV 👉 ML|DS|AI Webinar: ------------------------- https://www.youtube.com/playlist?list=PLE06V2Lg9GqOjFYw7XxIbvJLFBdiNpwZc 👉 Fundamentals of ML: -------------------- https://www.youtube.com/playlist?list=PLE06V2Lg9GqMANpojdMXRerSREyGnX9Oa 👉 Deep Learning Series: -------------------- https://www.youtube.com/playlist?list=PLE06V2Lg9GqPlq3Jk4X8Hg1B2i61b7GZo

What Is AI And Machine Learning || Artificial Intelligence || AI Marketing

What Is AI And Machine Learning || Artificial Intelligence || AI Marketing 00:00 Intriduction 03:06 What Is Al And Machine Learning? 07:26 Google As An Al-First Company 12:55 Preparing For Semantic Search 21:04 Big Data 25:12 Computer Version 27:19 Advertising 34:44 Email Marketing 42:18 Chatbots 45:35 Developing Your Al Skills - Using SQL 47:47 How To Future Proof Your Marketing artificial intelligence,artificial general intelligence,what is artificial intelligence,artificial intelligence explained,artificial intelligence documentary,learn artificial intelligence,artificial intelligence robot,artificial intelligence basics,artificial intelligence course,future artificial intelligence,artificial intelligence edureka,artificial inteligence,artificial intelligence tutorial,smartest artificial intelligence,ai marketing,ai marketing news,ai marketing scam,ai marketing avis,ai marketing france,ai marketing retrait,ai marketing arnaque,ai marketing cashback,ai marketing robot,ai marketing carte,ai marketing gains,ai marketing nouvelle,ai marketing 2022,ai marketing situation,ai marketing nouvelle 2022,شرح ai marketing,موقع ai marketing,اخبار ai marketing,اخر اخبار ai marketing,ai marketing شرح,ai marketing information 2022,ai marketing info what is ai,ai,what is artificial intelligence,what is ai technology,what is ai hindi,what is ai in hindi,what is ai marketing,what is ai?,what is artificial,ai explained,ai applications,what is artificial intelligence and why is it important,the rise of ai,what is artificial intelligence?,what is artificial intelligence (ai) in hindi,what is artificial intelligence in hindi?,artificial intelligence what is it,what is artificial intelligence in marketing,artificial intelligence,what is artificial intelligence,what is ai,artificial intelligence explained,artificial intelligence applications,artificial intelligence tutorial,artificial intelligence basics,types of artificial intelligence,what is ai technology,introduction to artificial intelligence,artificial intelligence tutorial for beginners,learn artificial intelligence,artificial intelligence edureka,simplilearn artificial intelligence

3D MRI brain segmentation - Made with TensorFlow.js

With a background in image processing and Machine Learning, Mohamed Masoud created ”Brain Chop”, a web-based end-to-end solution that can perform 3D MRI brain segmentation using TensorFlow.js. Learn how this tool is not only changing the world of medicine but how people interact with medical technology with its simple, user-friendly interface. Try it for yourself: Github: https://goo.gle/3wH69qq Live Demo: https://goo.gle/3PwuWpq Want to be on the show? Use #MadeWithTFJS to share your own creations on social media and we may feature you in our next show. Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow Go from zero to hero in Web ML via our $0 online course → https://goo.gle/learn-tfjs

How to customize TensorFlow Serving

TensorFlow Serving supports many additional features that you can leverage. Wei Wei, Developer Advocate at Google, discusses how you can customize TensorFlow Serving for your needs. He covers its integration with monitor tools, support for custom ops, how to configure the TF Serving model server, and more. Learn how TF Serving supports basic A/B tests and seamlessly integrates with Docker and Kubernetes to scale with demand. Resources: TensorFlow Serving configuration → https://goo.gle/3QxxvIF Prometheus → https://goo.gle/3N8XCmi Monitoring configuration → https://goo.gle/3N8XCmi Use TensorFlow Serving with Kubernetes → https://goo.gle/3zYvX4B Serving TensorFlow models with custom ops → https://goo.gle/3y7JvsY TensorFlow Serving model server flags → https://goo.gle/3tQ99Qq TensorFlow Serving metrics documentation → https://goo.gle/3NeAjY6 Docker Compose documentation →https://goo.gle/3xRuDxw Subscribe to TensorFlow → https://goo.gle/TensorFlow

[ML News] BLOOM: 176B Open-Source | Chinese Brain-Scale Computer | Meta AI: No Language Left Behind

#mlnews #bloom #ai Today we look at all the recent giant language models in the AI world! OUTLINE: 0:00 - Intro 0:55 - BLOOM: Open-Source 176B Language Model 5:25 - YALM 100B 5:40 - Chinese Brain-Scale Supercomputer 7:25 - Meta AI Translates over 200 Languages 10:05 - Reproducibility Crisis Workshop 10:55 - AI21 Raises $64M 11:50 - Ian Goodfellow leaves Apple 12:20 - Andrej Karpathy leaves Tesla 12:55 - Wordalle References: BLOOM: Open-Source 176B Language Model https://ift.tt/D58xNBR https://ift.tt/QyVt5Fz https://ift.tt/agvcYLn YALM 100B https://ift.tt/rp1SiRz Chinese Brain-Scale Supercomputer https://ift.tt/JgCjYdH https://ift.tt/E7k1Jfy Meta AI Translates over 200 Languages https://ift.tt/KL6Ybfx Reproducibility Crisis Workshop https://ift.tt/KVh8rU6 AI21 Raises $64M https://ift.tt/R89DpHC Ian Goodfellow leaves Apple https://twitter.com/goodfellow_ian/status/1544638709039091717 Andrey Karpathy leaves Tesla https://mobile.twitter.com/karpathy/status/1547332300186066944 https://ift.tt/1hPK3sV Wordalle https://ift.tt/0H4DKJv Links: Homepage: https://ykilcher.com Merch: https://ift.tt/nBkxdMI YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/z3oxSqF LinkedIn: https://ift.tt/zhMQdmO 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/c42nEIH Patreon: https://ift.tt/DxU0cXZ Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

NVIDIA’s AI Plays Minecraft After 33 Years of Training! 🤖

❤️ If you wish to support us and watch these videos in early access, check this out: - https://ift.tt/T2hMKvp 📝 The paper "MineDojo - Building Open-Ended Embodied Agents with Internet-Scale Knowledge" is available here: https://minedojo.org/ 🙏 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, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, 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/T2hMKvp Thumbnail background image credit: https://ift.tt/ehYOn6R Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Chapters: 0:00 Minecraft 0:15 GANCraft 1:31 AI playing games 1:52 NVIDIA tries Minecraft 2:20 But how? 3:12 Can this really work? 3:32 Teaching an AI English 4:19 1 - Exploration 4:48 2 - Building a fence 5:03 3 - Getting a bucket of lava 5:18 4 - Building a portal 5:32 5 - Final boss time 6:02 Long time horizons 6:25 More results 7:09 Does this really work? Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/peZx5c6 Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1JWtHag #minecraft

TensorFlow Serving client examples

Wei Wei, Developer Advocate at Google, walks through how to send REST and gRPC prediction requests to TensorFlow serving backend with Python and C++. Don’t worry if your client uses another language, below there are new sets of codelabs covering web, Android, Flutter, and iOS frontends. Codelabs Image Classification with TensorFlow Serving (Web) → https://goo.gle/3tQfqMb Object Detection with TensorFlow Serving (Android) → https://goo.gle/3Oyo7mf Text Classification with TensorFlow Serving (Flutter) → https://goo.gle/3OrvJGY Regression with TensorFlow Serving (iOS) → https://goo.gle/3xHHjXF Resources: TensorFlow Serving examples → https://goo.gle/3tRuAk1 gRPC → https://goo.gle/3y9jA4a gRPC Python basics tutorial → https://goo.gle/3HGdlb7 gRPC vs REST: Understanding gRPC, OpenAPI and REST and when to use them in API design → https://goo.gle/3bh8PUC Deploying Production ML Models with TensorFlow Serving playlist → Subscribe to TensorFlow → https://goo.gle/TensorFlow

NVIDIA GTC: When Simulation Becomes Reality! 🤯

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/w3h5oBb If everything goes well, this will be my GTC talk: https://ift.tt/3ajlLbc ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/Mfm9p7D - 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, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, 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/Mfm9p7D Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/x1e4zW8 Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/3fO8YSU

Finally, Robotic Telekinesis is Here! 🤖

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/Icb4QZd ❤️ Their mentioned post is available here: https://ift.tt/zWPgtX1 📝 The paper "Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on Youtube" is available here: https://ift.tt/OIqHjKl ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/VTmaieU - 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, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, 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/VTmaieU Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/uAD0aJ7 Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/CeOl7mX

The “spell check” of design systems - Made with TensorFlow.js

Meet Joo Hyung Park, a software engineer and product designer, from South Korea who is working on the “spell check” of design systems. His project, Figma ML, uses TensorFlow.js to go through four checkpoints to recognize key elements of UI design using a custom object detection API to determine if there are any design issues according to a given specification. Not only does this program help ensure the consistency of UI, it also automates the process of manually accounting for the countless design elements. Try it for yourself: Figma ML: https://goo.gle/3PnfKet GitHub: https://goo.gle/3lkkBz8 Want to be on the show? Use #MadeWithTFJS to share your own creations on social media and we may feature you in our next show. Catch more #MadeWithTFJS interviews → http://goo.gle/made-with-tfjs Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow Go from zero to hero in Web ML via our $0 online course → https://goo.gle/learn-tfjs

Deploying production ML models with TensorFlow Serving overview

Wei Wei, Developer Advocate at Google, overviews deploying ML models into production with TensorFlow Serving, a framework that makes it easy to serve the production ML models with low latency and high throughput. Learn how to start a TF Serving model server and send POST requests using the command line tool. Wei covers what it is, its architecture, general workflow, and how to use it. Stay tuned for the upcoming episodes on Deploying production ML models with TensorFlow Serving. Wei Wei will cover how to customize TF Serving, tune performance, perform A/B testing and monitoring, and more. Resources: TensorFlow Serving → https://goo.gle/3tLWkqr TensorFlow Serving with Docker → https://goo.gle/3tQHyi0 Training and serving a TensorFlow model with TF Serving → https://goo.gle/3HE2e2F Deploying Production ML Models with TensorFlow Serving playlist → Subscribe to TensorFlow → https://goo.gle/TensorFlow

NVIDIA’s New AI Trained For 10 Years! But How? 🤺

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/zr1g3h2 ❤️ Their mentioned post is available here (thank you Soumik!): http://wandb.me/ASE 📝 The paper "ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters" is available here: https://ift.tt/zWoGV43 📝 Our material synthesis paper with the latent space is available here: https://ift.tt/7UZ3YOH ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/JFx6h3i - 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, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, 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/JFx6h3i Chapters: 0:00 10 Years of training? 0:42 After 1 week 1:23 After 4 months 1:31 After 2 years 1:55 After 10 years! 2:25 How did they train for 10 years? 3:01 1. Latent spaces 3:52 2. Robust recovery 4:35 3. The controls are 👌 5:01 4. Adversaries 5:57 A great life lesson 6:15 The Third Law of Papers Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/dFnLZNv Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/trzaFfm

TensorFlow.js Community "Show & Tell" #7

6 new demos from the #MadeWithTFJS global community pushing the boundaries of what’s possible for Web MLv/ on device machine learning using JavaScript. Give your next web app superpowers in the browser and beyond. Hosted by Jason Mayes. Want to be on our next show? Use the #MadeWithTFJS tag on social to share your best TensorFlow.js creations, and we may feature you in our next show! Learn TensorFlow.js - zero to hero style → http://goo.gle/learn-tfjs Watch past #MadeWithTFJS​ interviews → http://goo.gle/made-with-tfjs TensorFlow.js Community Show & Tell series → http://goo.gle/tf-show-and-tell Links for this session’s presenters: 1) Mohamed Masoud - Brain Chop: Github: https://goo.gle/3wH69qq Live Demo: https://goo.gle/3PwuWpq 2) Ian Charnas - Punch out and animatronic backpack: Punch Out full video: https://goo.gle/3lkI4jB Animatronic backpack full video: https://goo.gle/3lnaQAc 3) Mariel Pettee - Beyond Imitation dancing autoencoder: Mirror exercise → https://goo.gle/3NwjRD7 Live demo and learn more: https://goo.gle/3FY0Ozd 4) Kevin Scott - Super Resolution in TensorFlow.js: Blog post write up: https://goo.gle/3wCDbb6 Upscaler.js: https://goo.gle/3wp5lr5 5) Benjamin Mularczyk - YOHA hand pose model: Github: https://goo.gle/3LpjEjJ Website / demo: https://goo.gle/3sIf5KP 6) Joo Hyung Park - Spell check for designers Figma ML: https://goo.gle/3PnfKet GitHub: https://goo.gle/3lkkBz8 Figma Community: https://goo.gle/3PnfKet Got questions about the show? Ask or connect with Jason on social: Twitter → https://goo.gle/3ePjPbj LinkedIn → https://goo.gle/38QkMfY Have a TensorFlow question? Ask it on the TensorFlow Forum → https://goo.gle/discuss_tensorflow Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow #TensorFlow

Ultimate Guide to Amazon SageMaker Studio

In this video, Denis Avdonin, a Senior Product Manager at Amazon, gives a tutorial on Amazon SageMaker Studio. He demonstrates how to create a notebook instance, how to install necessary libraries, and how to create and train a machine learning model. He also shows how to use SageMaker Studio to deploy the model and how to use it to make predictions. Join talks and AI Hackathons on Deep Learning Labs 👉🏼 https://lablab.ai/event Discord AI community 👥 https://discord.gg/XnxrJ8ytRs

OpenAI DALL-E 2 - Top 10 Best Images! 🤯

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/MRkscmf 📝 The paper "Hierarchical Text-Conditional Image Generation with CLIP Latents" is available here: https://ift.tt/NLzUe4S 🕊️ Follow us for more results on Twitter! https://twitter.com/twominutepapers 🧑‍🎨 Check out Felícia Zsolnai-Fehér's works: https://ift.tt/6bR18FU 🧑‍🎨 Judit Somogyvári's works: https://ift.tt/DJ1jzfA https://ift.tt/Ubq71K6 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/vTwSCIc - 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, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, 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/vTwSCIc Thumbnail background: OpenAI DALL-E 2 Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Links: Schrödinger cat: https://twitter.com/twominutepapers/status/1537871684979642369 Simulation: https://twitter.com/twominutepapers/status/1537125769528459268 Self portrait: https://twitter.com/twominutepapers/status/1538238078640340992 Office worker: https://twitter.com/twominutepapers/status/1536753218297929729 Angry tiger: https://twitter.com/twominutepapers/status/1538239586182344705 https://twitter.com/BellaRender/status/1538270897181802500/photo/1 Self portrait: https://twitter.com/twominutepapers/status/1538238078640340992 Cat falling into black hole: https://twitter.com/twominutepapers/status/1537548655246311424 Cat meme: https://twitter.com/OpDarkside/status/1537552199261118466 DND battle map: https://twitter.com/twominutepapers/status/1537098423152922624 Walrus: https://twitter.com/twominutepapers/status/1538258566838210561 Chomsky’s classic: https://twitter.com/twominutepapers/status/1538234269683810304 Bob Ross: https://ift.tt/6MENT3b Chapters: 0:00 - What is DALL-E 2? 0:56 - Novel images 1:40 - The Legendary Fox Scientist 2:14 - As good as an artist? 2:43 - Amazing new results! 3:08 - 1 3:25 - 2 3:43 - 3 4:10 - 4 4:26 - 5 4:57 - 6 5:27 - 7 5:37 - 8 6:22 - 9 6:35 - 10 7:07 - Plus 1 7:26 - Plus 2 7:70 - DALL-E 2 vs artist 8:13 - Changing the world Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/c8971rP Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/vBHZzuj #OpenAI #dalle

Amazon SageMaker Studio tutorial | Deep Learning Talks

In this video, Denis Avdonin, a Senior Product Manager at Amazon, gives a tutorial on Amazon SageMaker Studio. He demonstrates how to create a notebook instance, how to install necessary libraries, and how to create and train a machine learning model. He also shows how to use SageMaker Studio to deploy the model and how to use it to make predictions. Join talks and AI Hackathons on Deep Learning Labs 👉🏼 https://lablab.ai/event Discord AI community 👥 https://discord.gg/XnxrJ8ytRs

Watch This Dragon Grow Out Of Nothing! 🐲

❤️ Check out Cohere and sign up for free today: https://ift.tt/wT9BxWO 📝 The paper "Differentiable Signed Distance Function Rendering" is available here: https://ift.tt/pdk9DQP 📝 Our works on differentiable material synthesis and neural rendering are available here (with code): https://ift.tt/3zUvLPO https://ift.tt/HYdT9y7 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/h7wI5A1 - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Thank you Gordon Hanzmann-Johnson for catching an issue with a previous version of this video! 🙏 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, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, 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/h7wI5A1 Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/OLozICS Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/YG2eJ3T

NVIDIA’s AI Nailed Human Face Synthesis! 👩‍🎓

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/O0WHpEC ❤️ Their mentioned post is available here (Thank you Soumik!): https://ift.tt/oejq4i5 📝 The paper "StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators" is available here: https://ift.tt/BbmIKRW ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/mEygKGQ - 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, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, 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/mEygKGQ Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/Q83g2lG Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/CVrsu6q #nvidia #stylegan

JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained)

#jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels and use that as a world model to plan ahead in time. OUTLINE: 0:00 - Introduction 2:00 - Main Contributions 5:45 - Mode 1 and Mode 2 actors 15:40 - Self-Supervised Learning and Energy-Based Models 20:15 - Introducing latent variables 25:00 - The problem of collapse 29:50 - Contrastive vs regularized methods 36:00 - The JEPA architecture 47:00 - Hierarchical JEPA (H-JEPA) 53:00 - Broader relevance 56:00 - Summary & Comments Paper: https://ift.tt/edSrBxm Abstract: How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning. Author: Yann LeCun Links: Homepage: https://ykilcher.com Merch: https://ift.tt/FeELzsh YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/sbL8Bj0 LinkedIn: https://ift.tt/bCPeoOh 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/nlihB6S Patreon: https://ift.tt/eB5lr9R Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

tinyML Auto ML Tutorial with Nota AI

Auto ML Deep Dive Tutorial with Nota AI by Woneui Eric Hong. NetsPresso is a proprietary hardware-aware AI optimization platform which automates the development process of lightweight AI models. NetsPresso significantly reduces time and resources required to develop an AI model and optimizes it for the target device.

Real Time Machine Learning with Cloud Dataflow and Vertex AI || GSP275 || Problem Solved

Real Time Machine Learning with Cloud Dataflow and Vertex AI #learntoearn #machinelearning first lab

This is the first lab solution of third track learn_to_earn 2022 gcloud challenge #machinelearning #googleswags #dataanalytics skill lab: BigQuery Soccer Data Analysis #Sports_Data_Analysis Skills #learning #learn #skills #skills issue of pipeline in first lab has been diverted in this so please watch full vedio to get solution #dataanalytics skill challenge 2022 sign in :- https://www.cloudskillsboost.google/users/sign_in track-1 playlist link:- https://youtube.com/playlist?list=PLfN1vDZmNLHfpmPxIpsOPaTEGHv-iPthz join the game track-2: https://youtu.be/-rbFjKAnZUA please help to achieve the target of 1k likes and subscribers thank you.....

LABReal Time Machine Learning with Cloud Dataflow and Vertex AI | Learn to Earn Cloud Data Challenge

#RawnWarriors #EarnLearntoEarnCloudDataChallenge #learntoearncloudchallenge2022 #Freegoogleswags #qwiklabslearntoearncloudchallenge #cloudskillsboost ------------------------------------------------------------------------------------ Step1: : Login Open incognito window on your browser link: https://cloudskillsboost.google/ ------------------------------------------------------------------------------------ Step2: The Pre-requisite to be eligible for game prize: ---------------------------------------------------------------------------------- 1.Pre-requisite: Skill Badge (Code: 1q-l2e-prereq) 2.L2E3: Data Analyst Skills Track(Code: 1q-l2e3-595) https://go.qwiklabs.com/learn-to-earn ------------------------------------------------------------------------------------ Like and subscribe to the channel for awesome content! ------------------------------------------------------------------------------------ ------------- Support me By (it's Free) ------------------ LIKE | COMMENT | SHARE | SUBSCRIBE Subscribe to Our Channel For More Videos https://www.youtube.com/channel/UCA7nIMuVWHnhyl337r9EB8g FOLLOW ME ON: 📌Instagram:https://www.instagram.com/evil_swooping/ 📌website:http://sumansah.com.np/ 📌Facebook:https://www.facebook.com/devilswl 📌LinkedIn:https://www.linkedin.com/in/suman-shah/ 📌GitHub:https://github.com/suman-shah -----------When will I get the prize?----------- You’ll be contacted by August 31, 2022 with details to claim a prize (please ensure you're opted in, and consider adding us to your contacts). Please do not contact the Google Cloud Skills Boost support team about prizes.

tinyML Auto ML Tutorial with Nota AI

Auto ML Deep Dive Tutorial with Nota AI by Woneui Eric Hong. NetsPresso is a proprietary hardware-aware AI optimization platform which automates the development process of lightweight AI models. NetsPresso significantly reduces time and resources required to develop an AI model and optimizes it for the target device.

LABReal Time Machine Learning with Cloud Dataflow and Vertex AI | Learn to Earn Cloud Data Challenge

#RawnWarriors #EarnLearntoEarnCloudDataChallenge #learntoearncloudchallenge2022 #Freegoogleswags #qwiklabslearntoearncloudchallenge #cloudskillsboost ------------------------------------------------------------------------------------ Step1: : Login Open incognito window on your browser link: https://cloudskillsboost.google/ ------------------------------------------------------------------------------------ Step2: The Pre-requisite to be eligible for game prize: ---------------------------------------------------------------------------------- 1.Pre-requisite: Skill Badge (Code: 1q-l2e-prereq) 2.L2E3: Data Analyst Skills Track(Code: 1q-l2e3-595) https://go.qwiklabs.com/learn-to-earn ------------------------------------------------------------------------------------ Like and subscribe to the channel for awesome content! ------------------------------------------------------------------------------------ ------------- Support me By (it's Free) ------------------ LIKE | COMMENT | SHARE | SUBSCRIBE Subscribe to Our Channel For More Videos https://www.youtube.com/channel/UCA7nIMuVWHnhyl337r9EB8g FOLLOW ME ON: 📌Instagram:https://www.instagram.com/evil_swooping/ 📌website:http://sumansah.com.np/ 📌Facebook:https://www.facebook.com/devilswl 📌LinkedIn:https://www.linkedin.com/in/suman-shah/ 📌GitHub:https://github.com/suman-shah -----------When will I get the prize?----------- You’ll be contacted by August 31, 2022 with details to claim a prize (please ensure you're opted in, and consider adding us to your contacts). Please do not contact the Google Cloud Skills Boost support team about prizes.

Artificial intelligence | Artificial intelligence tutorial | Artificial intelligence course | AI

Artificial intelligence | Artificial intelligence tutorial | Artificial intelligence course #armetix #artificialintelligence #deeplearning #machinelearning #ann .. Hi This channel provide a information related science and Technology filed like AI, ML, ROBOTICS, Electronic, Electrical, Civil/Mechanical Design, Embedded systems, Projects and Practical Knowledge of all Engineering Domain. ---------------------------------------------------------------------------- Share a link other social media connecting platform-- Twitter-@ankrpvtltd Instagram-@armetixindore Telegram- Armetix Mail id- ankrpvtltd@gmail.com ---------------------------------------------------------------------------- #artificialintelligence

Altair Student Contest - Ball Balancing Table Example - Machine Learning in ROM AI and Activate

Hi guys, in this episode I want to share with you a tutorial on the Ball Balancing Table - an example which was used in the 2021 Student Contest hosted by Altair. Please find the mentioned additional material in the links below. BBT: https://youtu.be/tPr6-Rh0m-8 Altairs Recorded Webinar: https://youtu.be/dXgnzQcKD98 Activation Functions: https://bit.ly/3yDfFN6 Cross-Validation: https://bit.ly/3yaijsj Hope this helps you getting started with the contest. If you have any questions, doubts etc. just drop a comment or write a mail. I wish you all the best for the contest! Contest: http://altairuniversity.com/contest/ ▶ FOLLOW ME: Facebook: https://goo.gl/fSE2ta Google+: https://goo.gl/fdmNfK Twitter: https://goo.gl/ZSaqga Xing: https://goo.gl/TefJF5 LinkedIn: https://goo.gl/pnTfku ▶ SUPPORT ME: Patreon: https://goo.gl/qh9HLx ➤ MY EQUIPTMENT (affiliate links) Camera: https://amzn.to/3zxMRol Lights: https://amzn.to/3zAjW2Q Greenscreen: https://amzn.to/3zAjW2Q Controller: https://amzn.to/3zBGIrd Wacom Tablet https://amzn.to/2Hi1h0H

Artificial intelligence | Artificial intelligence tutorial | Artificial intelligence course | AI

Artificial intelligence | Artificial intelligence tutorial | Artificial intelligence course #armetix #artificialintelligence #deeplearning #machinelearning #ann .. Hi This channel provide a information related science and Technology filed like AI, ML, ROBOTICS, Electronic, Electrical, Civil/Mechanical Design, Embedded systems, Projects and Practical Knowledge of all Engineering Domain. ---------------------------------------------------------------------------- Share a link other social media connecting platform-- Twitter-@ankrpvtltd Instagram-@armetixindore Telegram- Armetix Mail id- ankrpvtltd@gmail.com ---------------------------------------------------------------------------- #artificialintelligence

Altair Student Contest - Ball Balancing Table Example - Machine Learning in ROM AI and Activate

Hi guys, in this episode I want to share with you a tutorial on the Ball Balancing Table - an example which was used in the 2021 Student Contest hosted by Altair. Please find the mentioned additional material in the links below. BBT: https://youtu.be/tPr6-Rh0m-8 Altairs Recorded Webinar: https://youtu.be/dXgnzQcKD98 Activation Functions: https://bit.ly/3yDfFN6 Cross-Validation: https://bit.ly/3yaijsj Hope this helps you getting started with the contest. If you have any questions, doubts etc. just drop a comment or write a mail. I wish you all the best for the contest! Contest: http://altairuniversity.com/contest/ ▶ FOLLOW ME: Facebook: https://goo.gl/fSE2ta Google+: https://goo.gl/fdmNfK Twitter: https://goo.gl/ZSaqga Xing: https://goo.gl/TefJF5 LinkedIn: https://goo.gl/pnTfku ▶ SUPPORT ME: Patreon: https://goo.gl/qh9HLx ➤ MY EQUIPTMENT (affiliate links) Camera: https://amzn.to/3zxMRol Lights: https://amzn.to/3zAjW2Q Greenscreen: https://amzn.to/3zAjW2Q Controller: https://amzn.to/3zBGIrd Wacom Tablet https://amzn.to/2Hi1h0H

ARC Challenge Live Coding

Chatting & Coding