Artificial Intelligence vs Machine Learning vs Deep Learning

Complete Roadmap to Become a Data Scientist in 2022: https://youtu.be/lHKlCqx94OY Roadmap to Become a Data Analyst in 2022: https://youtu.be/024KMhTKlVs Definition of AI: https://en.wikipedia.org/wiki/Artificial_intelligence Image source: http://www.journalimcms.org/special_issue/an-empirical-science-research-on-bioinformatics-in-machine-learning/ Here's my favorite resources: Best Courses for Analytics: --------------------------------------------------------------------------------------------------------- + Google Analytics: https://coursera.pxf.io/x9jJe3 + IBM Data Science: https://coursera.pxf.io/9WjVmY + SQL Basics: https://coursera.pxf.io/5bDkWo Best Courses for Programming: --------------------------------------------------------------------------------------------------------- + Data Science in R: https://coursera.pxf.io/LPy0jV + Python for Everybody: https://coursera.pxf.io/QOvYaa + Data Structures & Algorithms: https://coursera.pxf.io/9WjVm4 Best Courses for Machine Learning: --------------------------------------------------------------------------------------------------------- + Math Prerequisites: https://coursera.pxf.io/kj14D0 + Machine Learning: https://coursera.pxf.io/Ao1gxK + Deep Learning: https://coursera.pxf.io/Jrvke2 + ML Ops: https://coursera.pxf.io/gbeRMg Best Courses for Statistics: --------------------------------------------------------------------------------------------------------- + Statistics with Python: https://coursera.pxf.io/Xxv40G + Statistics with R: https://coursera.pxf.io/za5zZ0 Best Courses for Big Data: --------------------------------------------------------------------------------------------------------- + Google Cloud Data Engineering: https://coursera.pxf.io/0JMGEO + AWS Data Science: https://coursera.pxf.io/n1Z4rV + Big Data Specialization: https://coursera.pxf.io/ORvYVZ More Courses: --------------------------------------------------------------------------------------------------------- + Tableau: https://coursera.pxf.io/3Pqk6r + Excel: https://coursera.pxf.io/RyN0gX + Computer Vision: https://coursera.pxf.io/doPyNW + Natural Language Processing: https://coursera.pxf.io/YgvVEe + IBM Dev Ops: https://coursera.pxf.io/KevBa7 + IBM Full Stack Cloud: https://coursera.pxf.io/mgJ497 + Object Oriented Programming: https://coursera.pxf.io/x9jJVx Become a Member of the Channel! https://bit.ly/3oOMrVH Follow me on LinkedIn! https://www.linkedin.com/in/greghogg/ Full Disclosure: Please note that I may earn a commission for purchases made at the above sites! I strongly believe in the material provided; I only recommend what I truly think is great. If you do choose to make purchases through these links; thank you for supporting the channel, it helps me make more free content like this!

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos (Paper Explained)

#openai #vpt #minecraft Minecraft is one of the harder challenges any RL agent could face. Episodes are long, and the world is procedurally generated, complex, and huge. Further, the action space is a keyboard and a mouse, which has to be operated only given the game's video input. OpenAI tackles this challenge using Video PreTraining, leveraging a small set of contractor data in order to pseudo-label a giant corpus of scraped footage of gameplay. The pre-trained model is highly capable in basic game mechanics and can be fine-tuned much better than a blank slate model. This is the first Minecraft agent that achieves the elusive goal of crafting a diamond pickaxe all by itself. OUTLINE: 0:00 - Intro 3:50 - How to spend money most effectively? 8:20 - Getting a large dataset with labels 14:40 - Model architecture 19:20 - Experimental results and fine-tuning 25:40 - Reinforcement Learning to the Diamond Pickaxe 30:00 - Final comments and hardware Blog: https://ift.tt/fnAXi9Q Paper: https://ift.tt/oDTYNEz Code & Model weights: https://ift.tt/1z5K4t7 Abstract: Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities. However, for many sequential decision domains such as robotics, video games, and computer use, publicly available data does not contain the labels required to train behavioral priors in the same way. We extend the internet-scale pretraining paradigm to sequential decision domains through semi-supervised imitation learning wherein agents learn to act by watching online unlabeled videos. Specifically, we show that with a small amount of labeled data we can train an inverse dynamics model accurate enough to label a huge unlabeled source of online data -- here, online videos of people playing Minecraft -- from which we can then train a general behavioral prior. Despite using the native human interface (mouse and keyboard at 20Hz), we show that this behavioral prior has nontrivial zero-shot capabilities and that it can be fine-tuned, with both imitation learning and reinforcement learning, to hard-exploration tasks that are impossible to learn from scratch via reinforcement learning. For many tasks our models exhibit human-level performance, and we are the first to report computer agents that can craft diamond tools, which can take proficient humans upwards of 20 minutes (24,000 environment actions) of gameplay to accomplish. Authors: Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune Links: Homepage: https://ykilcher.com Merch: https://ift.tt/Oagxdp0 YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/xK6QRIF LinkedIn: https://ift.tt/SRdQou1 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/1QaMGtP Patreon: https://ift.tt/KVsGNng Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Google AI Simulates Evolution On A Computer! 🦖

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/zYkB8uH ❤️ Their mentioned post is available here (Thank you Soumik Rakshit!): https://ift.tt/de9wCGP 📝 The paper "Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts" is available here: https://ift.tt/9X674Qk ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/7OwVHTL - 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, Javier Bustamante, 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/7OwVHTL Thumbnail image: DALL-E 2. Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/R5u8L9J Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/LMcjTI1

What is quantum machine learning and how can we use it?, with Luis Serrano

What is quantum machine learning and how can we use it?, with Luis Serrano at NDR - The AI Conf, June 2021 Ever wondered how quantum computers work, and how do they do machine learning? With quantum computing technologies nearing the ear of commercialization and quantum advantage, machine learning has been proposed as one of the most promising applications. One of the areas in which quantum computing is showing great potential is in generative models in unsupervised and semi-supervised learning. In this talk, you'll learn the basics of generative machine learning, quantum computing, and how the two come together. No previous knowledge of quantum computing and generative models is needed for this talk. _____ Luis Serrano - Quantum AI Research Scientist at Zapata Computing, Book Author Luis Serrano is a Quantum AI Research Scientist at Zapata Computing. He is the author of the book Grokking Machine Learning and maintains a popular YouTube channel where he explains machine learning in pedestrian terms (https://serrano.academy). Luis has previously worked in machine learning at Apple and Google, and at Udacity as the head of content for AI and data science. He has a PhD in mathematics from the University of Michigan, a master’s and bachelors from the University of Waterloo, and worked as a postdoctoral researcher in mathematics at the University of Quebec at Montreal. #NDR #ML #AI #machinelearning #artificialintelligence

Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

This course will give you an introduction to machine learning concepts and neural network implementation using Python and TensorFlow. Kylie Ying explains basic concepts, such as classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. She then demonstrates how to implement a feedforward neural network to predict whether someone has diabetes, as well as two different neural net architectures to classify wine reviews. ✏️ Course created by Kylie Ying. 🎥 YouTube: https://youtube.com/ycubed 🐦 Twitter: https://twitter.com/kylieyying 📷 Instagram: https://instagram.com/kylieyying/ This course was made possible by a grant from Google's TensorFlow team. ⭐️ Resources ⭐️ 💻 Datasets: https://drive.google.com/drive/folders/1YnxDqNIqM2Xr1Dlgv5pYsE6dYJ9MGxcM?usp=sharing 💻 Feedforward NN colab notebook: https://colab.research.google.com/drive/1UxmeNX_MaIO0ni26cg9H6mtJcRFafWiR?usp=sharing 💻 Wine review colab notebook: https://colab.research.google.com/drive/1yO7EgCYSN3KW8hzDTz809nzNmacjBBXX?usp=sharing ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:00:34) Colab intro (importing wine dataset) ⌨️ (0:07:48) What is machine learning? ⌨️ (0:14:00) Features (inputs) ⌨️ (0:20:22) Outputs (predictions) ⌨️ (0:25:05) Anatomy of a dataset ⌨️ (0:30:22) Assessing performance ⌨️ (0:35:01) Neural nets ⌨️ (0:48:50) Tensorflow ⌨️ (0:50:45) Colab (feedforward network using diabetes dataset) ⌨️ (1:21:15) Recurrent neural networks ⌨️ (1:26:20) Colab (text classification networks using wine dataset) -- 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp

Introduction to AWS SageMaker | AI & Machine Learning in AWS | AWS Tutorial for Beginners

In this video, we will see AWS SageMaker and how it works. #AWS #SageMaker #AI #Machinelearning If you have any questions or doubts you can ask in the comment section below. Do like and share this video if you find this video helpful. Subscribe to this channel and hit the bell icon to get a notification for upcoming videos. ▬▬▬▬▬▬ Want to learn more? ▬▬▬▬▬▬ Full Terraform tutorial ► https://bit.ly/2GwK8V2 DevOps Tools, like Ansible ► https://bit.ly/3iASHuP Docker Tutorial ► https://bit.ly/3iAT9Jx AWS Tutorial ► https://bit.ly/30GFv1q GCP Tutorial ► https://bit.ly/3mwh412 Jenkins Tutorials ► https://bit.ly/3iHnfv4 Jenkins Pipeline ► https://bit.ly/30CJGLB Python ► https://bit.ly/3I7bewU Python in just 1 video ► https://bit.ly/3EeqGVy ▬▬▬▬▬▬ Free Udemy Courses ▬▬▬▬▬▬ AWS Solution Architect ► https://bit.ly/3nsL2lZ Terraform Tutorial ► https://bit.ly/3ix68w0 Ansible Tutorial ► https://bit.ly/3d8eFEl Jenkins Tutorial ► https://bit.ly/3ix6wdW ▬▬▬▬▬▬ Connect with me ▬▬▬▬▬▬ Youtube Subscription ► https://bit.ly/2LENtS1 Facebook Page: ► https://www.facebook.com/EasyAWSLearn/ Demo Reference: ► https://github.com/easyawslearn Blog: ► https://easyawslearn.blogspot.com/ Medium: ► https://techworldwithvijaypatel.mediu... Dev: ► https://dev.to/easyawslearn

Introduction to AWS SageMaker | AI & Machine Learning in AWS | AWS Tutorial for Beginners

In this video, we will see AWS SageMaker and how it works. #AWS #SageMaker #AI #Machinelearning If you have any questions or doubts you can ask in the comment section below. Do like and share this video if you find this video helpful. Subscribe to this channel and hit the bell icon to get a notification for upcoming videos. ▬▬▬▬▬▬ Want to learn more? ▬▬▬▬▬▬ Full Terraform tutorial ► https://bit.ly/2GwK8V2 DevOps Tools, like Ansible ► https://bit.ly/3iASHuP Docker Tutorial ► https://bit.ly/3iAT9Jx AWS Tutorial ► https://bit.ly/30GFv1q GCP Tutorial ► https://bit.ly/3mwh412 Jenkins Tutorials ► https://bit.ly/3iHnfv4 Jenkins Pipeline ► https://bit.ly/30CJGLB Python ► https://bit.ly/3I7bewU Python in just 1 video ► https://bit.ly/3EeqGVy ▬▬▬▬▬▬ Free Udemy Courses ▬▬▬▬▬▬ AWS Solution Architect ► https://bit.ly/3nsL2lZ Terraform Tutorial ► https://bit.ly/3ix68w0 Ansible Tutorial ► https://bit.ly/3d8eFEl Jenkins Tutorial ► https://bit.ly/3ix6wdW ▬▬▬▬▬▬ Connect with me ▬▬▬▬▬▬ Youtube Subscription ► https://bit.ly/2LENtS1 Facebook Page: ► https://www.facebook.com/EasyAWSLearn/ Demo Reference: ► https://github.com/easyawslearn Blog: ► https://easyawslearn.blogspot.com/ Medium: ► https://techworldwithvijaypatel.mediu... Dev: ► https://dev.to/easyawslearn

ML monitoring with Evidently. A tutorial from CS 329S: Machine Learning Systems Design.

00:00 Introduction to the Tutorial 00:30 Speaker Introduction 01:05 What ML monitoring setup depends on 02:40 How to design ML monitoring 03:58 Toy example: bike demand monitoring 05:08 Code example starts 06:02 Dataset preparation 06:29 Model training 07:45 Model validation. Regression performance dashboard. 13:18 Production model training. Shorter version of the report. 14:55 Week 1 of production use. 17:30 Week 2 of production use. Choice of widgets. 19:08 Week 3 of production use. Model quality drop. 20:10 Quality drop debugging. Data drift dashboard. 24:43 Dashboard customization. Statistical tests, bins, tabs. 31:33 How to automate batch monitoring. MLflow example. 36:43 Q: How can I share reports with my coworker? 37:43 Q: What other features are most requested? 38:44 Q: Are standard deviations useful only for normal distributions? Code example: https://github.com/evidentlyai/evidently/blob/main/examples/data_stories/bicycle_demand_monitoring_setup.ipynb Information about the course: https://stanford-cs329s.github.io/syllabus.html

Google’s New Robot: Your Personal Butler! 🤖

❤️ Check out Cohere and sign up for free today: https://ift.tt/mPlOnIy 📝 The paper "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances" is available here: https://ift.tt/NGuk3IS https://ift.tt/XBL3yKZ Check us out on Twitter for more DALL-E 2 related content! https://twitter.com/twominutepapers ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/CZzQlm6 - 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, 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/CZzQlm6 Thumbnail image: OpenAI DALL-E 2 Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/7Sf0Uul Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/ByX4Los

Mini-Tutorial 1: Data Integrity For Deep Learning Models

Presenters: Victoria Gerardi and John Cilli (US Army, CCDC Armaments Center) Deep learning models are built from algorithm frameworks that fit parameters over a large set of structured historical examples. Model robustness relies heavily on the accuracy and quality of the input training datasets. This mini-tutorial seeks to explore the practical implications of data quality issues when attempting to build reliable and accurate deep learning models. The tutorial will review the basics of neural networks, model building, and then dive deep into examples and data quality considerations using practical examples. An understanding of data integrity and data quality is pivotal for verification and validation of deep learning models, and this tutorial will provide students with a foundation of this topic.

OpenAI’s DALL-E 2: Even More Beautiful Results! 🤯

❤️ Train a neural network and track your experiments with Weights & Biases here: https://ift.tt/8dpbv4P 📝 The paper "Hierarchical Text-Conditional Image Generation with CLIP Latents" is available here: https://ift.tt/0QckfOU 📝 Our Separable Subsurface Scattering paper with Activision-Blizzard: https://ift.tt/WCIXHUM 📝Our earlier papers with the caustics: https://ift.tt/fNacv49 https://ift.tt/Kfmt7vo Try it out: https://ift.tt/zDuq37l (once again, note that this is a reduced version. it also runs through gradio, which is pretty cool, check it out!) ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/W14s0Vu - 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, 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/W14s0Vu Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu 00:00 What is DALL-E 2? 01:40 DALL-E 1 vs DALL-E 2 02:15 1 - It can make videos 03:04 2 - Leonardo da Apple 03:19 3 - Robot learns a new language 03:28 4 - New AI-generated drinks! 04:12 5 - Toilet car 04:27 6 - Lightbulbs! 04:56 7 - Murals 05:11 8 - Darth Ant 05:23 9 - Text! 06:14 10 - Pro photography 06:28 Subsurface scattering! 07:10 Try it out yourself! 07:45 Changing the world Tweet sources: Fantasy novel: https://twitter.com/wenquai/status/1527312285152452608 Leonardo: https://twitter.com/nin_artificial/status/1524330744600055808/photo/1 Encyclopedia: https://twitter.com/giacaglia/status/1513271094215467008?s=21 Modernize: https://twitter.com/model_mechanic/status/1513588042145021953 Robot learning: https://twitter.com/AravSrinivas/status/1514217698447663109 Drinks: https://twitter.com/djbaskin/status/1514735924826963981 Toilet car: https://twitter.com/PaulYacoubian/status/1514955904659173387/photo/2 Lightbulb: https://twitter.com/mattgroh/status/1513837678172778498 Murals: https://twitter.com/_dschnurr/status/1516449112673071106/photo/1 Darth Ant: https://twitter.com/hardmaru/status/1519224830989684738 Sign: https://twitter.com/npew/status/1520050727770488833/photo/1 Hand: https://twitter.com/graycrawford/status/1521755209667555328 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/rRji0zG Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/ZEgBUsm

NVIDIA Renders Millions of Light Sources! 🔅

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/nS7C3aL 📝 The paper "Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lighting" is available here: https://ift.tt/94ACqGc 📝 Our earlier paper with the scene that took 3 weeks - https://ift.tt/123JPFk The 2D light transport webapp: https://ift.tt/uZ8NriB ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/cB64jaw - 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, 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/cB64jaw Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/cF4rHRm Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/OgoWRkJ