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Today’s Top Tech Trends – April 05th 2023

Today's Top Tech Trends by Djamgatech and ChatGPT

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Today’s Top Tech Trends – April 05th 2023.
 
 

Today’s Top Tech Trends – April 05th 2023: Summary

In the rapidly evolving world of technology, Substack is introducing a new short-form ‘Notes’ feature that bears a striking resemblance to Twitter. This new offering aims to provide users with a fresh platform for sharing thoughts and ideas, sparking interest among content creators and consumers alike.

Entertainment enthusiasts have reason to celebrate, as many canceled HBO shows, including ‘Westworld’ and ‘Raised by Wolves,’ are now available on the Roku platform. This move allows fans to catch up on their favorite series and provides Roku users with an even richer library of content.

The automotive industry is moving full speed ahead with innovation, as the 2025 all-electric Ram 1500 Rev boasts a massive battery that could revolutionize the electric vehicle market. Additionally, the 2024 Hyundai Kona is attracting attention with its affordable price tag and over-the-air updates, making cutting-edge technology more accessible to a broader audience.

Mozart Data has announced a free tier on its platform, encouraging smaller businesses to leverage data analytics and transform their operations. By making their services more accessible, Mozart Data is helping to level the playing field between small enterprises and larger competitors.

In the creator economy, Pico, a Creator CRM company, has rebranded itself as Hype and raised $10 million in funding. This development is generating excitement among artists and influencers eager to harness the power of the rebranded platform to grow their online presence and manage their careers.

The US is grappling with a challenge in the crypto community, as it struggles to retain top blockchain developers who are seeking safer havens for their work. These talented individuals are exploring opportunities abroad, looking for more supportive environments for their innovative projects.

In immigration news, many startup founders and employees are seeking advice on how to transfer their H-1B visas and green cards to their new ventures. With experts like Sophie providing guidance, these individuals can navigate the complexities of the immigration process and contribute to the thriving tech ecosystem.

Coast, a demo platform for API-first companies, has secured $2.1 million in funding, enabling more businesses to develop, test, and showcase their API solutions. Meanwhile, Verto, a digital banking platform, has announced that a quarter of SVB customers operating in Africa have opened accounts with them, highlighting the rapid growth of the fintech sector.

April 05th top tech trends showcase the ongoing innovation and expansion within the industry, as new platforms, services, and developments continue to shape the way we live, work, and connect with one another.

Storized by ChatGPT-4

#TechTrendsToday, #SubstackNotes, #HBOonRoku, #ElectricVehicles, #MozartData, #HypeCRM, #CryptoTalent, #ImmigrationStartups, #APIPlatform, #FintechGrowt

Today's Top Tech Trends - April 05th 2023 - AI Unraveled
Today’s Top Tech Trends – April 05th 2023 – AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

Today’s Top Tech Trends – April 05th 2023: Top Tech Trends links

Substack’s new short-form ‘Notes’ feed looks a lot like Twitter;

Many canceled HBO shows, including ‘Westworld’ and ‘Raised by Wolves,’ are now on Roku;

The 2025 all-electric Ram 1500 Rev has an absolutely massive battery;

Mozart Data announces free tier to encourage smaller businesses to get on platform;

Creator CRM company Pico rebrands to Hype, raises $10 million;

The US is losing crypto talent as blockchain devs seek safer havens;

The 2024 Hyundai Kona will be one the most affordable cars with over-the-air updates;

Ask Sophie: How do we transfer H-1Bs and green cards to our startup?;

Coast, a demo platform for ‘API-first’ companies, lands $2.1M;

Verto claims a quarter of SVB customers operating in Africa are opening accounts on its platform;

Tech executive Bob Lee dead after apparent stabbing attack in San Francisco 
 

Sony is reportedly developing a new PlayStation handheld;

ChatGPT vs Google Bard: Which is better? We put them to the test.;

Open garage doors anywhere in the world by exploiting this “smart” device;

Users fume after My Cloud network breach locks them out of their data;

Hackers exploit WordPress plugin flaw that gives full control of millions of sites;

These angry Dutch farmers really hate Microsoft;

3CX knew its app was flagged as malicious but took no action for 7 days;

AI-generated video of Will Smith eating spaghetti astounds with terrible beauty;

Trojanized Windows and Mac apps rain down on 3CX users in massive supply chain attack;

Pro-Russian hackers target elected US officials supporting Ukraine;

Fearing “loss of control,” AI critics call for 6-month pause in AI development;

Top Android Trends on April 05th, 2023

Apple reveals details of its first store in India;

vivo T2 5G and T2x 5G’s India launch date announced, will be different from Chinese models;

Google Now Launcher will stop working in April;

New Peloton Watch App lets you use your Wear OS 3+ watch as a heart rate monitor;

Samsung Galaxy Tab S9 Ultra chipset, battery details revealed;

vivo X Fold2 certified with 120W fast charging;

Samsung Galaxy Z Flip5 and Galaxy Z Fold5’s colors tipped;

Samsung Free to become Samsung News;

Samsung Galaxy A54 lands at Verizon on April 6;

Top iPhone iOs Trends on April 05th, 2023

11-inch M2 iPad Pro lands at best price of the year in Wednesday’s best deals, Apple Watch Series 8 from $329, more;

Apple Pay promo offers free McNuggets at McDonald’s, here’s how to get ’em;

Apple Pay Later ‘early access’ rollout moving slowly, here’s how to know if you’re included;

How to turn off iPhone WiFi auto-join for public and carrier networks;

Super Mario iPhone games might not have a future, suggests Miyamoto interview;

Apple Music trademark cannot cover live performances, rules court;

Apple says it’s closer than ever to having a completely carbon neutral supply chain;

Here’s how fast users would replace a broken/lost iPhone, iPad, Mac – what about you? [Poll];

Apple tiger teams reexamining supply chain, down to screws and plastic inserts;

Foxconn earnings show 21% fall in March, expects further decrease this quarter;

 

Today’s Top Tech Trends – April 05th 2023: Beautiful Data of the day

Most spoken languages in the world

Today's Top Tech Trends - April 05th 2023: Most spoken languages in the world
Today’s Top Tech Trends – April 05th 2023: Most spoken languages in the world

4 emerging technologies you need to know about via Gartner...

1. Smart world expands with fusion of physical-digital experiences.
2. Productivity accelerates with #AI advances.
3. Transparency/privacy get scrutiny amid exponential growth in data collection.
4. New critical tech enablers create new business + monetization opportunities

4 emerging technologies you need to know about via Gartner: Today's Top Tech Trends - April 05th 2023
4 emerging technologies you need to know about via Gartner: Today’s Top Tech Trends – April 05th 2023

Latest AI Trends in April 2023

Can AI Really Predict Lottery Results? We Asked an Expert.

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2000+ Science Quiz and Trivia and Brain Teaser

Science Quiz and Trivia

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Looking for a fun and interactive way to learn more about science? Look no further than the Science Quiz Trivia and Brain Teaser App and Game! This essential learning tool is packed with more than 2000 quiz questions on various science topics, making it the perfect way to challenge yourself or others on your knowledge of the world around us. With multiple choice answers and engaging visuals, the Science Quiz Trivia and Brain Teaser App and Game is perfect for kids and adults alike. So what are you waiting for? Test your science smarts today with the Science Quiz Trivia and Brain Teaser App and Game!

The Science Quiz Trivia and Brain Teaser App and Game is ideal for anyone who loves science! With more than 2000 questions on general science topics, this app is perfect for kids and adults alike. Challenge yourself with the Ultimate Science Quiz, or test your knowledge with the General Science Quiz Game. With multiple choice answers and a host of different categories, there’s something for everyone in this comprehensive quiz game. Learn about biology, geology, physics, chemistry, zoology, computer science, and space with the World Science Quiz. The multilingual capabilities of this game make it accessible to even more people around the globe. With so much to offer, the Science Quiz Trivia and Brain Teaser App and Game is a must-have for any trivia lover!

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Think you know everything there is to know about science? Prove it with this Science Quiz Trivia and Brain Teaser App and Game! With more than 2000 different questions, this app is sure to test your knowledge. And with multiple choice answers, it’s easier than ever to get the correct answer. Whether you’re a science nerd or just trying to learn more about the world around you, this is the perfect app for you. So what are you waiting for? Download now and start quizzing!

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US History Quiz Trivia and Brain Teaser App and Game

2000+ US History Quiz Trivia

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US History Quiz Trivia and Brain Teaser App and Game is an amazing way to test your knowledge of American history. With more than 2000 quiz questions, it’s perfect for anyone who wants to learn more about the United States. The questions cover everything from the Founding Fathers to present day, so you can brush up on your history anytime, anywhere. With the US History Quiz Trivia and Brain Teaser App and Game, you can impress your friends and family with your vast knowledge of all things America!

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iOs: https://apps.apple.com/ca/app/all-subjects-quiz-brain-teaser/id1603487636

Android: https://play.google.com/store/apps/details?id=com.quizandbrainteaserfree.app

Windows/Microsoft: https://www.microsoft.com/store/apps/9PML05P5X85B

Amazon #ushistory #unitedstates #history #quiz

https://vimeo.com/737192093

Are you looking for a fun and engaging way to learn about US history? Look no further than the US History Quiz Trivia and Brain Teaser App and Game! This app is packed with more than 2000 trivia questions about the United States, from its inception to present day. Test your knowledge and see how much you really know about this great country! The US History Quiz Trivia and Brain Teaser App and Game is perfect for anyone who wants to learn more about US history, or simply brush up on their knowledge. So what are you waiting for? Download the app today and start learning!

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iOs: https://apps.apple.com/ca/app/all-subjects-quiz-brain-teaser/id1603487636

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Windows/Microsoft: https://www.microsoft.com/store/apps/9PML05P5X85B

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Features:

  • 2000+ US History Quiz
  • Updated regularly
  • Hint for Answers
  • Illustrated
  • Score tracker
  • Timer

Are you looking for a fun and engaging way to learn about US history? Look no further than the US History Quiz Trivia and Brain Teaser App and Game! This app is packed with more than 2000 trivia questions about the United States, from its inception to present day. Test your knowledge and see how much you really know about this great country! The US History Quiz Trivia and Brain Teaser App and Game is perfect for anyone who wants to learn more about US history, or simply brush up on their knowledge. So what are you waiting for? Download the app today and start learning!

USA History Quiz Trivia and Brain Teaser by Djamgatech
US History Quiz and Trivia

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iOs: https://apps.apple.com/ca/app/all-subjects-quiz-brain-teaser/id1603487636

Android: https://play.google.com/store/apps/details?id=com.quizandbrainteaserfree.app

Windows/Microsoft: https://www.microsoft.com/store/apps/9PML05P5X85B

Amazon #ushistory #unitedstates #history #quiz

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Geography Quiz Trivia Brain Teaser App and Game – Multilingual

World Geography Quiz Trivia

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Think you know your geography? Put your knowledge to the test with this fun and challenging trivia app! With over 1,000 questions on everything from world flags to major landmarks, you’ll never run out of things to learn. And with four difficulty levels, there’s something for everyone. So whether you’re a geography whiz or just looking to brush up on your knowledge, this is the perfect app for you. So download now and start exploring the world!

All Subjects Quiz Trivia Brain Teaser – Geography

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iOs: https://apps.apple.com/ca/app/all-subjects-quiz-brain-teaser/id1603487636

Android: https://play.google.com/store/apps/details?id=com.quizandbrainteaserfree.app

Windows/Microsoft: https://www.microsoft.com/store/apps/9PML05P5X85B

Amazon Web: https://www.amazon.com/gp/product/B09RCW3F5J

Amazon Android: https://www.amazon.com/gp/product/B09RCQKHYG

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We provide an enjoyable and educational experience for people of all ages with our fun and interactive quizzes.

Our app offers a variety of different quizzes on world geography, including quizzes on flags, maps, and countries. We also have quizzes specifically for kids and adults, so everyone can learn something new.

With our Geography Quiz Trivia Brain Teaser App, you’ll be able to test your knowledge on everything from the world map to the different countries in Europe. So whether you’re a geography enthusiast or just looking to learn something new, our app is perfect for you!

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Mathematics Quiz Trivia Brain Teaser Game – General Math and SAT Math Quiz for All

All Subjects Quiz Trivia Brain Teaser - Math

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In this post, we will be featuring a general math quiz and SAT math quiz that is perfect for all ages and levels of learners. Whether you’re a student preparing for your next test, a kid who wants to brush up on your basic math skills, or just someone who loves playing trivia games, we think you’ll find this quiz fun and challenging. So put on your thinking cap and get ready to solve some math problems! And don’t forget to share your score in the comments section below!.

Looking for a fun and challenging math game? Look no further than this Math Quiz Trivia and Brain Teaser App! This app is perfect for anyone who wants to brush up on their math skills or just have some fun with challenging math puzzles. With games covering addition, subtraction, multiplication, and division, there is something for everyone. And with the ability to track your progress and compete against friends, you’ll always have something to strive for. So why not give Math Quiz Trivia and Brain Teaser App a try today?

General Math practice game for brain training!

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iOs: https://apps.apple.com/ca/app/all-subjects-quiz-brain-teaser/id1603487636

Android: https://play.google.com/store/apps/details?id=com.quizandbrainteaserfree.app

Windows/Microsoft: https://www.microsoft.com/store/apps/9PML05P5X85B Amazon

Web: https://www.amazon.com/gp/product/B09RCW3F5J

Amazon Android: https://www.amazon.com/gp/product/B09RCQKHYG

Free and educational math quiz suitable for all ages of people! Online trainer for an active brain. Improve problem solving skills by playing! Free Math Games for everyone from kids to adults. Maths practice game to train your brain & is designed for all ages including kids, girls and boys, adults including parents and grandparents. Kid’s math is the smallest in size maths App on Google Play!

Easiest multiplication and division games with Addition and Subtraction games all in one app. Increase your brain power with an excellent educational game for learning mathematics for kids and adults of all ages. Math games for 1st grade, 2nd grade, 3rd grade, 4th grade, 5th grade, or 6th grade and of course, any teenager or adult who is interested in training their brain and improving their math skills!

Math Games, Learn Add, Subtract, Multiply & Divide. The Math Trivia Quiz Game is a Fun Math Trivia Game containing Brainy Math Puzzles & Quizzes which helps you to learn basic problems, equations, sequences, series, etc.

All Subjects Quiz Trivia Brain Teaser Math will help you develop abstract and logical thinking, develop perseverance, sharpen the intellect, ability to analyze, raise IQ, and memory. This Math Trivia Consists of multiple levels from simple to complex. With each level, it gets more complicated and more interesting.

HOW TO PLAY:

– Tap the RIGHT answer button from the 4 multiple choices;

– Select the correct answer before the time expires.

– There are 20 quizzes for each level,

– Try a HINT to help you remove a wrong

Features:

– Easy and quick to play. Multilingual.

– Unique math quizzes & puzzles

– Answer over 1000 math trivia questions!

– Completely free & designed for all ages

– Improve memory, focus, and mental speed

– Free trivia game to test your IQ and math knowledge!

FREE DOWNLOAD today and refresh your mind with the math melody!

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Amazon Web: https://www.amazon.com/gp/product/B09RCW3F5J

Amazon Android: https://www.amazon.com/gp/product/B09RCQKHYG

Math Quiz Trivia Brain Teaser

Math Quiz Trivia and Brain Teaser App is an educational game that helps you learn basic math skills while testing your knowledge with fun trivia questions. The app includes addition, subtraction, multiplication, and division games, as well as a variety of general math and SAT math questions. With this app, you can sharpen your math skills while also developing your abstract and logical thinking. So download now and see how high you can score on the Math Quiz Trivia and Brain Teaser App!

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iOs: https://apps.apple.com/ca/app/all-subjects-quiz-brain-teaser/id1603487636

Android: https://play.google.com/store/apps/details?id=com.quizandbrainteaserfree.app

Windows/Microsoft: https://www.microsoft.com/store/apps/9PML05P5X85B

Amazon Web: https://www.amazon.com/gp/product/B09RCW3F5J

Amazon Android: https://www.amazon.com/gp/product/B09RCQKHYG

If you’re looking for a math app that’s both educational and fun, look no further than Math Quiz Trivia and Brain Teaser! This app features multiplication and division games, as well as addition and subtraction games, all in one handy app. With Math Quiz, you’ll learn basic problems, equations, sequences, and series while sharpening your abstract and logical thinking skills. And because it’s a quiz game, you’ll stay engaged and entertained while you improve your math IQ. So download now and start mastering math with Math Quiz Trivia and Brain Teaser!

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Soccer Football World Cup Champion’s League Soccer Football Quiz and Trivia App

Soccer Football World Cup Champion's League Trivia and Quiz

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Soccer Football World Cup Champion’s League Soccer Football Quiz.

Are you a big soccer fan? Do you know everything there is to know about the beautiful game? Put your knowledge to the test with our Soccer Football Quiz Trivia app!

With over 1,000 questions on everything from the latest soccer news to the history of the sport, we’ll keep you entertained for hours. And if you’re ever stuck, you can always use our handy hint system to give yourself a little nudge in the right direction.

Our Soccer Football Quiz Trivia app is also great for keeping up with the latest news from around the world of soccer. We’ve got all the latest breaking stories, tweets, and results, so you’ll never miss a beat.

Soccer Football Quiz Triviahttps://awscloudpractitionerexamprep.com/quizandbrainteaser/

It’s time to test your knowledge about the world’s most popular sport – football (or soccer, depending on where you’re from)! The Football Soccer World Cup Champions League Quiz Trivia and Breaking News App is the ultimate way to stay up-to-date on everything related to the beautiful game. From latest news and tweets, to quizzes and trivia, this app has it all. And if that’s not enough, you can also Guess the Soccer Player, Guess the Footballer, and even Guess the Soccer Team! So what are you waiting for? Download now and start showing off your football knowledge to all your friends!

Download The App Below:

iOs: https://apps.apple.com/ca/app/all-subjects-quiz-brain-teaser/id1603487636

Android: https://play.google.com/store/apps/details?id=com.quizandbrainteaserfree.app

Web: https://inrealtimenow.com/soccer_football/ or here

Windows/Microsoft: https://www.microsoft.com/store/apps/9PML05P5X85B

Amazon Web: https://www.amazon.com/gp/product/B09RCW3F5J

Amazon Android: https://www.amazon.com/gp/product/B09RCQKHYG

App Preview:

Soccer Football World Cup Champion’s League Quiz and Trivia App

App Features:

Soccer Quiz TriviaFootball Quiz and Trivia,

Guess the Soccer Player Quiz

Guess the Footballer Trivia and Quiz

World Football Quiz 2022

Guess the Soccer Team Quiz

Guess the Football Manager Trivia

Football Teams Logo Quiz

Soccer Teams Anthems Trivia

World Cup trivia

Champions League Quiz

Premier League Quiz

La Liga quiz

Calcio trivia

Bundesliga Trivia

Pro Football Quiz

Pro Soccer Trivia

Guess the Football Legend Quiz

Guess the football manager quiz

Guess the soccer coach trivia

Soccer clubs logo quiz

Ligue 1 Quiz

MLS soccer quiz

Football transfer quiz

Soccer Top goals scorers quiz

Copa America quiz

Afcon Quiz

UEFA cup quiz

Asian soccer cup quiz

Manchester United Quiz

Liverpool FC trivia

Real Madrid FC quiz

Barcelona FC quiz

Selecao quiz

Ballon d’or quiz

Lionel Messi Quiz

Cristiano Ronal Quiz

Kylian Mbappe Quiz

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Machine Learning For Dummies

Machine Learning For Dummies

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The Machine Learning For Dummies App is the perfect way to learn about Machine Learning, AI and how to Elevate your Brain. With over 400+ Machine Learning Operations, Basic and Advanced ML questions and answers, the latest ML news, and a daily Quiz, the App is perfect for anyone who wants to learn more about this exciting field.

With operations on AWS, Azure, and GCP, the App is perfect for beginners and experts alike. And with its updated daily content, you’ll always be up-to-date on the latest in Machine Learning. So whether you’re a beginner or an expert, the Machine Learning For Dummies App is the perfect way to learn more about this fascinating field. Use this App to learn about Machine Learning and Elevate your Brain with Machine Learning Quiz, Cheat Sheets, Questions and Answers updated daily.

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ML For Dummies on iOs [Contain Ads]

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The App provides:

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– 100+ Machine Learning Basics Questions and Answers

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– Machine Learning Interview Questions and Answers

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Machine Learning Quiz For Dummies
Machine Learning Quiz For Dummies

The App covers: Azure AI Fundamentals AI-900 Exam Prep: Azure AI 900, ML, Natural Language Processing, Modeling, Data Engineering, Computer Vision, Exploratory Data Analysis, ML implementation and Operations, S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, GCP PROFESSIONAL Machine Learning Engineer, Framing ML problems, Architecting ML solutions, Designing data preparation and processing systems, Developing ML models, Monitoring, optimizing, and maintaining ML solutions, Automating and orchestrating ML pipelines, Quiz and Brain Teaser for AWS Machine Learning MLS-C01, Cloud Build, Kubeflow, TensorFlow, CSV, JSON, IMG, parquet or databases, Hadoop/Spark, Vertex AI Prediction, Describe Artificial Intelligence workloads and considerations, Describe fundamental principles of machine learning on Azure, Describe features of computer vision workloads on Azure, Describe features of Natural Language Processing (NLP) workloads on Azure , Describe features of conversational AI workloads on Azure, QnA Maker service, Language Understanding service (LUIS), Speech service, Translator Text service, Form Recognizer service, Face service, Custom Vision service, Computer Vision service, facial detection, facial recognition, and facial analysis solutions, optical character recognition solutions, object detection solutions, image classification solutions, azure Machine Learning designer, automated ML UI, conversational AI workloads, anomaly detection workloads, forecasting workloads identify features of anomaly detection work, NLP, Kafka, SQl, NoSQL, Python, DocumentDB, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

Note and disclaimer: We are not affiliated with Microsoft or Azure or Google or Amazon. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Download the Machine Learning For Dummies App below:

ML For Dummies on iOs [Contain Ads]

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ML PRO without ADS on Windows [No Ads, More Features]

ML PRO For Web/Android

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Azure AI Fundamentals AI-900 Exam Preparation

Azure AI Fundamentals AI-900 Exam Prep PRO

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Azure AI Fundamentals AI-900 Exam Preparation: Azure AI 900 is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure. This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

This Azure AI Fundamentals AI-900 Exam Preparation App provides Basics and Advanced Machine Learning Quizzes and Practice Exams on Azure, Azure Machine Learning Job Interviews Questions and Answers, Machine Learning Cheat Sheets.

Download Azure AI 900 on iOs

Download Azure AI 900 on Windows10/11

Azure AI Fundamentals Exam Prep

Azure AI Fundamentals AI-900 Exam Preparation App Features:

– Azure AI-900 Questions and Detailed Answers and References

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– NLP and Computer Vision Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

Azure AI Fundamentals AI-900 Exam Preparation
Azure AI 900 – Machine Learning

This Azure AI Fundamentals AI-900 Exam Prep App covers:

  • ML implementation and Operations,
  • Describe Artificial Intelligence workloads and considerations,
  • Describe fundamental principles of machine learning on Azure,
  • Describe features of computer vision workloads on Azure,
  • Describe features of Natural Language Processing (NLP) workloads on Azure ,
  • Describe features of conversational AI workloads on Azure,
  • QnA Maker service, Language Understanding service (LUIS), Speech service, Translator Text service, Form Recognizer service, Face service, Custom Vision service, Computer Vision service, facial detection, facial recognition, and facial analysis solutions, optical character recognition solutions, object detection solutions, image classification solutions, azure Machine Learning designer, automated ML UI, conversational AI workloads, anomaly detection workloads, forecasting workloads identify features of anomaly detection work, Kafka, SQl, NoSQL, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.
  • This App can help you:
  • – Identify features of common AI workloads
  • – identify prediction/forecasting workloads
  • – identify features of anomaly detection workloads
  • – identify computer vision workloads
  • – identify natural language processing or knowledge mining workloads
  • – identify conversational AI workloads
  • – Identify guiding principles for responsible AI
  • – describe considerations for fairness in an AI solution
  • – describe considerations for reliability and safety in an AI solution
  • – describe considerations for privacy and security in an AI solution
  • – describe considerations for inclusiveness in an AI solution
  • – describe considerations for transparency in an AI solution
  • – describe considerations for accountability in an AI solution
  • – Identify common types of computer vision solution:
  • – Identify Azure tools and services for computer vision tasks
  • – identify features and uses for key phrase extraction
  • – identify features and uses for entity recognition
  • – identify features and uses for sentiment analysis
  • – identify features and uses for language modeling
  • – identify features and uses for speech recognition and synthesis
  • – identify features and uses for translation
  • – identify capabilities of the Text Analytics service
  • – identify capabilities of the Language Understanding service (LUIS)
  • – etc.

Download Azure AI 900 on iOs

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Azure AI Fundamentals Breaking News – Azure AI Fundamentals Certifications Testimonials

  • Is there a good audio study material for the AZ-900?
    by /u/ballandabiscuit (Microsoft Azure Certifications) on April 30, 2024 at 6:33 pm

    My best study time is when I’m driving and at the gym so I usually listen to audio-based study materials. Anyone know of anything good for the AZ-900? I’m looking at MS Learn materials now but it’s mostly reading. submitted by /u/ballandabiscuit [link] [comments]

  • AI-102 vs DP-100
    by /u/According_Ice6515 (Microsoft Azure Certifications) on April 30, 2024 at 6:28 pm

    Just wondering which one is better to do first after AI-900? AI Engineer or Data Scientist? Thank you all! submitted by /u/According_Ice6515 [link] [comments]

  • Barely Passed AZ 104
    by /u/Ok-Goose9586 (Microsoft Azure Certifications) on April 30, 2024 at 5:01 pm

    First try, score is 727. I'm happy, yet at the same time disappointed. I studied so hard for 2 weeks, 8 to 10 hours/day. Watched all Scott Duffy course, then John Savill cram video, then practice tests, just to barely pass. submitted by /u/Ok-Goose9586 [link] [comments]

  • I can’t see a VM in a sub
    by /u/Knyghttt (Microsoft Azure) on April 30, 2024 at 3:42 pm

    Hi all, so I have a it of a weird issue. We normally have this partnership with a company where we create the sub and they create the solution. For some reason for this one sub (I have full ownership) I can’t see the VM at all…so I can’t add the reservation. No one who has access to the sub and global admin can see the vm but the account that created it can see it. Is there a setting on the vm that hides it away from people? I’m still learning azure so I’m not that experienced in it, so forgive me if my terminology or explanations are incorrect. Any help is much appreciated 🙂 I also can see other resources in the sub, but not this specific vm. submitted by /u/Knyghttt [link] [comments]

  • What annoys and surprises you the most when comparing Azure to AWS?
    by /u/Affectionate-Dig403 (Microsoft Azure) on April 30, 2024 at 3:16 pm

    I've been using AWS for over 5 years and I'm comfortable with their services. I've only been on Azure for 6 months, but I'm really impressed with how well it integrates with Azure Active Directory (AAD) and Entra. This makes managing user access much easier than using AWS's native services. The only downside I've found so far is that Azure's documentation can be a bit tough to navigate compared to AWS. It makes learning the platform a little more challenging. submitted by /u/Affectionate-Dig403 [link] [comments]

  • P2S VPN needed in a full cloud only scenario?
    by /u/purplepersonality (Microsoft Azure) on April 30, 2024 at 3:00 pm

    I’m currently working with a consultant to set up infrastructure in Azure for my company that will replace every on-prem server. I already set up a virtual Meraki firewall (vMX) for a S2S VPN connection and planned on using it for P2S VPN on the client laptops too. The consultant I’m working with is now saying that the P2S VPN isn’t needed since the client laptops just need access to Azure file shares and virtual desktop remote apps and that authentication can happen at login or via specific conditional access policies. So basically the cloud infrastructure is protected by Azure Bastion and users can only connect from enrolled devices with 2FA authentication without VPN. Is this just as secure? What are the advantages of using a vpn instead? Thanks! submitted by /u/purplepersonality [link] [comments]

  • Two 50% vouchers; could I use both ?
    by /u/MeBe-Me (Microsoft Azure Certifications) on April 30, 2024 at 2:13 pm

    Hi all, I just attended the Azure Fundamentals Virtual Training Day & the Azure Data Fundamentals Virtual Training Day. I was already sent a 50% voucher to take the Azure Fundamentals exam, but still waiting for the 50% voucher for the Azure Data Fundamentals exam. I also intend to complete the "30 Days to Learn It" challenge to have a 50% Voucher for the Azure Data Engineering Exam. In the FAQ of the "30 Days to Learn It", it is noted that " Each participant is only eligible to receive one (1) discounted Microsoft Certification exam code every six months". I don't know if it only concerns the "30 Days to Learn It" challenges. My questions : 1 - Could I have and use all of these vouchers in a 6 month period ? 2 - Will I receive the 50% voucher for Azure Data Fundamentals Exam? (since I already have a 50% voucher for the other exam) 3 - Could I use those 2 vouchers to take both exams ? (It must be used within 90 days) 4 - Could I still be able to receive the 50% voucher for completing the "30 Days to Learn It" challenge and use it asap after using at least one of the first vouchers (for the other exams) ? Thanks submitted by /u/MeBe-Me [link] [comments]

  • App Sevice with Azure Functions
    by /u/ArtistFit6282 (Microsoft Azure) on April 30, 2024 at 2:04 pm

    Does it make sense to use App Service to host just FE but use functions for BE purposes or is that silly bc just use the app service hosting for BE per usual... submitted by /u/ArtistFit6282 [link] [comments]

  • I messed up the Entra config - anyway around?
    by /u/cgsmith105 (Microsoft Azure) on April 30, 2024 at 1:37 pm

    I messed up the Entra configuration. It redirects to Google Signon with "Taking you to your organization's sign-in page...". I try signing into Google then it redirects me back to Microsoft and I get this error. I cannot log into Entra admin or Azure admin. What can I do to get me through so I can just reset and start over Edit: I was trying to configure Entra to use Google as the Auth and user management... this doesn't seem possible though so I am just going to configure it properly. https://preview.redd.it/fc0yg77ldmxc1.png?width=692&format=png&auto=webp&s=d4bfbf77e86c03562d94dfe2f92dec6751a9ac2f submitted by /u/cgsmith105 [link] [comments]

  • Can an External Azure AD account login to an Azure AD Joined workstation of the cross tenant
    by /u/zratedls1 (Microsoft Azure) on April 30, 2024 at 1:24 pm

    I've been searching online and cannot find an answer to this question and can not get this to work in practice. Scenario: Tenant A is configured as the source for Entra external identities cross tenant synchronization to Tenant B. An account from Tenant A has been successfully sync'd to Tenant B. The sync'd account shows the following properties in Tenant B's Entra: User Type: Member Identities: ExternalAzureAD Creation Type: Invitation Invitation State: Accepted Can the account sync'd from Tenant A log into an Azure AD Joined workstation of Tenant B? I've tried logging into the workstation with the UPN from Azure (TenantAusername#EXT#@TenantB.onmicrosoft.com). I've also tried logging in with the UPN from Tenant A. I am able to add the UPN of the sync'd account from tenant B to the local administrator group on the Azure AD Joined workstation in Tenant B, so it's validating the account exists but I cant seem to get logged in. Has anyone done this before? is it possible? submitted by /u/zratedls1 [link] [comments]

  • CA in azure?
    by /u/darkjmarider (Microsoft Azure) on April 30, 2024 at 1:23 pm

    Looking through some options right now to configure certificate-based authentication with entra id and was wondering if there was any service within azure that I could use for this to happen. Right now, our environment is fully cloud based and we want to keep it that way. I would rather not throw up a VM if I can prevent it. We want to be able to use yubikeys on the local computers and use our entra accounts for the uac prompt and use the yubkey to authentication. We want to block the credential provider as well on the computers so there is only one option to login as an admin. submitted by /u/darkjmarider [link] [comments]

  • Migrating Group Policy to Azure from onsite AD, help needed
    by /u/DifferenceJazzlike40 (Microsoft Azure) on April 30, 2024 at 12:46 pm

    Hi everyone, I'm pretty new to Azure Group Policy and need some help with this, I'm a domain admin of the on site domain and want to export the policies from the onsite AD so i can migrate them to azure. The plan is too get this domain completely moved from onsite to Azure (we have less then 50 users). I've managed to backup the GPO to a folder and have copied that to my local machine, but whatever i've tried to do to upload it to azure just doesn't seem to work. I've created a few policies myself but i'm probably missing loads (a lot that probably need forgetting about). We're using a .local domain on the dc and the new one will use a fqdn for the azure one. I'm just in the test phase and realise we need to move documents etc, but if i can get the AD completed we'll be a whole lot further along. Appreciate the help submitted by /u/DifferenceJazzlike40 [link] [comments]

  • unable to access Azure Files shares
    by /u/angriusdogius (Microsoft Azure) on April 30, 2024 at 12:33 pm

    I am wondering if someone could help with this issue. We have an issue were new VMs cannot access our Azure Files shares. The current config: We have Azure ADDS set up with servers joined to this. Users exist in Azure ADDS, servers and users can access Azure Files shares. The desired config: We are moving back to Domain Controller VMs in Azure. Why are we doing this? Because we are having issues with SSO / password synchronising etc and have been advised that this is the best solution. So far we have a new DC, joined to the new domain. This server cannot access our existing Azure Files shares. When trying to access them we don't even get prompted for access credentials. When trying to ping / nslookup the storage account, it resolves to the external IP. If I specify the ADDS DNS IP it resolves to the private link as expected. The correct IPs are configured in Private DNS Zones. What am I missing? What else should I be checking please? submitted by /u/angriusdogius [link] [comments]

  • Azure vWan and vHub clarification (Address Space)
    by /u/Oracle4TW (Microsoft Azure) on April 30, 2024 at 12:20 pm

    Hi all, documentation seems light on this topic. When moving to a vWAN, you create virtual hubs. When doing so, it asks for an address space in CIDR using a /23 space. What isn't clear is if this should be in the same address space as allocated to the whole of Azure. So let's say I'm using a 127.16.0.0/19 range for the whole of azure, do the virtual hub(s) need to be in that space? Most of the documentation never mentions is, and just says it doesn't actually matter as it's all MS managed and taken care of. submitted by /u/Oracle4TW [link] [comments]

  • Anyone having trouble with lighthouse email alerts?
    by /u/ls3c6 (Microsoft Azure) on April 30, 2024 at 12:15 pm

    I changed the receiving email address for my risky sign-in alert trigger and now I receive no email alerts at all for it. I've deleted the rule and recreated, yet no email alerts work now. Was working fine before. Any ideas? submitted by /u/ls3c6 [link] [comments]

  • Azure price increase vs consumption increase - and commitment threshold
    by /u/symmetricchaos (Microsoft Azure) on April 30, 2024 at 12:09 pm

    Hi. One of my customers recently requested me to have a look at their Azure spend. From 1st of February 2024 Azure increased the price of Azure services by 12% for all services in my region to match our local currency to the US dollar. My customer requests an analysis comparing what part of their cost increase is due to increased consumption, and what part of their cost increase is due to the price change. The intention of this is to have some data when renegotiating an Azure agreement/pricing with Microsoft. they also request a suggestion for a threshold/commitment consumption (where they commit to a certain amount of money they "guarantee" spending each month) to get a discount from Microsoft. If they don't spend this amount, they have to pay the remaining sum. Example: If they commit to spending 10K USD per month and only spend 8K, they have to pay 2K extra. However if they spend 12K, they get a discount on either the entire sum or the 2K "more" they have spent (unsure about this to be honest - have to ask the customer.) Is this a reasonable analysis to perform? In the case of 1., I'm a little bit confused as the price increase is clearly stated as 12% - can't I just subtract 12% from their monthly spend after February, and account the remaining increase/decrease to change in actual consumption? Am I misunderstanding this? In the case of 2: is there a reasonable approach/method that anybody can recommend so I can a advise some volume to commit to? submitted by /u/symmetricchaos [link] [comments]

  • Storage account clean up
    by /u/Gloomy-Lab4934 (Microsoft Azure) on April 30, 2024 at 11:14 am

    Folks, need help to find a way to list all storage accounts that are in active for more than 90 days, what is the criteria? submitted by /u/Gloomy-Lab4934 [link] [comments]

  • [Teach Tuesday] Share any resources that you've used to improve your knowledge in Azure in this thread!
    by /u/AutoModerator (Microsoft Azure) on April 30, 2024 at 11:00 am

    All content in this thread must be free and accessible to anyone. No links to paid content, services, or consulting groups. No affiliate links, no sponsored content, etc... you get the idea. Found something useful? Share it below! submitted by /u/AutoModerator [link] [comments]

  • Access to XtremeLabs and Skillable
    by /u/de_Rham (Microsoft Azure Certifications) on April 30, 2024 at 10:52 am

    Is it possible to buy labs from these sites or do I have to be a member of a particular course, group etc.? I've created accounts but there doesn't seem to be a purchase option. XtremeLabs says it needs an account approval. I don't see a way to buy labs in Skillable either. submitted by /u/de_Rham [link] [comments]

  • AZ-900 Practice Exams
    by /u/p4ttl1992 (Microsoft Azure Certifications) on April 30, 2024 at 10:52 am

    Are there any practice exam websites that change up the questions each time? I've read through the learn content but would like to practice on the exams a fair bit but the practice assessment on the website is repeating the same questions.... submitted by /u/p4ttl1992 [link] [comments]

Download Azure AI 900 on iOs

Download Azure AI 900 on Windows10/11

Posted on

AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

You can translate the content of this page by selecting a language in the select box.

The AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

AWS MLS-C01 Machine Learning Specialty Exam Prep PRO

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AWS machine learning certification prep
AWS machine learning certification prep

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault

tolerance.

Recommend and implement the appropriate machine learning services and features for a given

problem.

Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

Note and disclaimer: We are not affiliated with Microsoft or Azure or Google or Amazon. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

  • [D] ChatGPT is just glorified autocorrect
    by /u/Alarmed-Fee6193 (Machine Learning) on April 30, 2024 at 8:19 am

    From what I understand of GPT and other LLMs, what they essentially do, is just predict the next token given a sequence of tokens. No reasoning, just cold hard statistics. For this reason, I believe that programmers are still decades away from being replaced by AI. Especially by LLM based AI like Devin. Please, change my mind EDIT: I am currently getting my MSc in Data Science with a dissertation on Generative AI in robotics and I want to understand more about it, thanks! submitted by /u/Alarmed-Fee6193 [link] [comments]

  • [R] NExT: Teaching Large Language Models to Reason about Code Execution
    by /u/SeawaterFlows (Machine Learning) on April 30, 2024 at 7:53 am

    Paper: https://arxiv.org/abs/2404.14662 Abstract: A fundamental skill among human developers is the ability to understand and reason about program execution. As an example, a programmer can mentally simulate code execution in natural language to debug and repair code (aka. rubber duck debugging). However, large language models (LLMs) of code are typically trained on the surface textual form of programs, thus may lack a semantic understanding of how programs execute at run-time. To address this issue, we propose NExT, a method to teach LLMs to inspect the execution traces of programs (variable states of executed lines) and reason about their run-time behavior through chain-of-thought (CoT) rationales. Specifically, NExT uses self-training to bootstrap a synthetic training set of execution-aware rationales that lead to correct task solutions (e.g., fixed programs) without laborious manual annotation. Experiments on program repair tasks based on MBPP and HumanEval demonstrate that NExT improves the fix rate of a PaLM 2 model, by 26.1% and 14.3% absolute, respectively, with significantly improved rationale quality as verified by automated metrics and human raters. Our model can also generalize to scenarios where program traces are absent at test-time. submitted by /u/SeawaterFlows [link] [comments]

  • [R] "PiShield: A NeSy Framework for Learning with Requirements" - [we explore three application scenarios: functional genomics, autonomous driving, and tabular data generation]
    by /u/SeawaterFlows (Machine Learning) on April 30, 2024 at 7:37 am

    Paper: https://arxiv.org/abs/2402.18285 Code: https://github.com/mihaela-stoian/PiShield Following from the work: 1.) "How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data" (Paper / Code) 2.) "CCN+: A neuro-symbolic framework for deep learning with requirements" (Paper / Code) Abstract: Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first framework ever allowing for the integration of the requirements into the neural networks' topology. PiShield guarantees compliance with these requirements, regardless of input. Additionally, it allows for integrating requirements both at inference and/or training time, depending on the practitioners' needs. Given the widespread application of deep learning, there is a growing need for frameworks allowing for the integration of the requirements across various domains. Here, we explore three application scenarios: functional genomics, autonomous driving, and tabular data generation. submitted by /u/SeawaterFlows [link] [comments]

  • [D] Foundational papers for Graph Adversarial Learning?
    by /u/ADDMYRSN (Machine Learning) on April 30, 2024 at 4:10 am

    I've been self teaching myself graph theory and have really been enjoying it. My job is going to take a pivot towards adversarial ML, so I was curious on the applications of graph theory and adversarial ML. I found the following resource: https://github.com/safe-graph/graph-adversarial-learning-literature which seems to be quite useful. Though, I'd like to make sure I have the fundamentals covered first before diving into anything published in the last year or two. Does anyone have recommended papers I could read to start out understanding this topic? Thank you! submitted by /u/ADDMYRSN [link] [comments]

  • [P] Seeking advice on video-to-text architecture for a specific use case
    by /u/kaku53 (Machine Learning) on April 30, 2024 at 1:43 am

    Hey fellow Redditors, I'm working on a project that involves extracting text information from video sequences. The goal is to develop a model that can accurately transcribe text from videos, similar to how speech-to-text models work for audio. I've been exploring different architectures, but I'm not sure which one would be the most suitable for my use case. I'd love to hear from experts in the field and get some advice on the best approach. To give you a better idea, let's say I'm working on a project that involves transcribing text from videos of people cooking recipes. The videos show the chef preparing a dish, and I want to extract the recipe text from the video. I've considered using a combination of CNNs and transformers, but I'm not sure if this is the best approach. I'd appreciate any advice on the following: What type of architecture would be most suitable for this task? Are there any pre-trained models that I can fine-tune for my specific use case? What are some common pitfalls to avoid when developing a video-to-text model? Thanks in advance for your help and advice! Edit: I'd like to clarify that I'm looking for general advice on video-to-text architectures, not specifically for the cooking recipe example. I'm interested in learning about different approaches and techniques that can be applied to various video-to-text tasks. submitted by /u/kaku53 [link] [comments]

  • [P] i need the source code of a research project
    by /u/Emotional-Rhubarb725 (Machine Learning) on April 29, 2024 at 11:49 pm

    https://domingomery.ing.puc.cl/material/gdxray/ I am working on an x-ray image analysis project and I need the source code of the project I proveded but I can't find it any where If you can help me finding it or provide any source where I can search it myself submitted by /u/Emotional-Rhubarb725 [link] [comments]

  • [D] What the best way to resolve QLORA tuned model forgetting ?
    by /u/snassimr (Machine Learning) on April 29, 2024 at 11:16 pm

    I am close to deploying my QLORA-tuned LLM in a specific domain. The fine-tuned model demonstrates performance superior to GPT-4 in generating answers for customers on specific tasks. However, I’ve noticed an issue: the fine-tuned LLM sometimes fails to provide coherent answers to general questions, and more importantly, it fails to provide safe answers. I'm aware of the solution that involves incorporating general-domain data during fine-tuning. However, I'm quite skeptical about this approach since it may require a significant amount of data and resources. I'm more optimistic about using a properly instruction-tuned model to interact with customers. If the customer's intention is to perform a specific task, we could re-route their request to the fine-tuned LLM. I believe this strategy aligns well with the agentic view for developing LLM-based applications. I would appreciate your professional opinion on this topic. submitted by /u/snassimr [link] [comments]

  • [R] Proposed framework for state-of-the-art, AI models with mlp And error correction integration
    by /u/KnowgodsloveAI (Machine Learning) on April 29, 2024 at 9:01 pm

    Hey folks, long-time lurker and AI researcher here. I've been dabbling in neural networks and machine learning long enough to witness first-hand the evolution from simple perceptrons to today's GPT-3 and beyond. Today, I want to share a framework I've been working on that aims to significantly enhance AI's strategic and reflective capabilities by integrating GPT-2 with GRU networks alongside some new twists on error correction and forecasting mechanisms. Advanced Strategic AI Framework Enhanced GPT-2 and GRU Integration The first hurdle was the direct use of GPT-2 outputs for GRU layers, which led to inefficiencies due to mismatched outputs and inputs. GRUs expect a certain dimensionality that GPT-2's outputs don't naturally align with. Solution: I introduced an adapter layer to reshape and normalize GPT-2 outputs making them more suitable for the GRU layers. This not only improves data flow but also helps in stabilizing the learning process by smoothing out variances. Advanced Error Prediction and Correction Using basic CNNs for error prediction in strategic AI tasks just doesn’t cut it, given the complexity and dynamic nature of such tasks. Solution: A Transformer-based encoder layer now handles error detection and correction. It’s fantastic for this role because it captures dependencies and nuances in context far better, enhancing the model's ability to self-correct based on dynamic inputs. Complex Strategic Foresight Layer Linear models are too primitive and fail to handle the complex forecasting needs of a truly strategic AI. Solution: I opted for a multi-layer perceptron (MLP) with non-linear activation functions. This setup is more adept at projecting future states from both current and historical data, offering a richer, more nuanced strategic output. Hybrid Training Paradigms Meta-learning and scenario-based reinforcement learning are great but need to be meticulously crafted to work well, especially in unpredictable environments. Solution: The training regime now combines supervised, unsupervised, and reinforcement learning techniques. This comprehensive approach lets the AI not only learn general representations but also quickly adapt to new, unseen scenarios and optimize strategies continuously based on performance feedback. Ethical Considerations and Modeling Often overlooked, ethical reasoning capabilities in AI are crucial, especially as these systems are more frequently applied in critical and high-stakes environments. Solution: I developed a dedicated ethical reasoning module that utilizes a mix of rule-based systems and machine learning models trained on ethical dilemmas. This continuously assesses and adjusts the AI's strategies to maintain alignment with evolving ethical standards. Implementation Code Let's dive into the actual code that makes this happen: ```python import torch from torch import nn from transformers import GPT2Model, GPT2Config class AdvancedStrategicModel(nn.Module): def init(self, modelname='gpt2', num_heads=8, num_layers=2, hidden_dim=768): super(AdvancedStrategicModel, self).init_() self.gpt2 = GPT2Model.from_pretrained(model_name) # Initialize with pre-trained GPT-2 self.adapter = nn.Linear(hidden_dim, hidden_dim) # Adapter layer to match GPT-2 output with GRU input needs self.gru = nn.GRU(input_size=hidden_dim, hidden_size=hidden_dim, num_layers=num_layers, batch_first=True) # GRU layer # Transformer encoder layer for error prediction and correction self.error_predictor = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=num_heads) self.error_correction = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=num_heads), num_layers=1) # Strategy output layer self.strategy_layer = nn.Sequential( nn.Linear(hidden_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim) ) def forward(self, input_ids): outputs = self.gpt2(input_ids) # Get outputs from GPT-2 hidden_states = outputs.last_hidden_state adapted_states = self.adapter(hidden_states) # Adapt states for GRU processing gru_states, _ = self.gru(adapted_states) # Apply GRU for temporal dynamics error_signals = self.error_correction(self.error_predictor(gru_states)) # Error processing corrected_states = gru_states + error_signals # Apply corrections strategic_output = self.strategy_layer(corrected_states) # Generate strategic output return strategic_output, corrected_states Example usage model = AdvancedStrategicModel() input_ids = torch.tensor([[121, 582, 1034, ...]], dtype=torch.long) # Example tokenized input IDs strategic_output, corrected_states = model(input_ids) ``` . submitted by /u/KnowgodsloveAI [link] [comments]

  • Develop and train large models cost-efficiently with Metaflow and AWS Trainium
    by Ville Tuulos (AWS Machine Learning Blog) on April 29, 2024 at 7:20 pm

    This is a guest post co-authored with Ville Tuulos (Co-founder and CEO) and Eddie Mattia (Data Scientist) of Outerbounds. To build a production-grade AI system today (for example, to do multilingual sentiment analysis of customer support conversations), what are the primary technical challenges? Historically, natural language processing (NLP) would be a primary research and development

  • Cohere Command R and R+ are now available in Amazon SageMaker JumpStart
    by Pradeep Prabhakaran (AWS Machine Learning Blog) on April 29, 2024 at 5:47 pm

    This blog post is co-written with Pradeep Prabhakaran from Cohere.  Today, we are excited to announce that Cohere Command R and R+ foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. Command R/R+ are the state-of-the-art retrieval augmented generation (RAG)-optimized models designed to tackle enterprise-grade workloads. In this post, we walk through how

  • [D] Suggestions for NLP Papers Commonly Implemented in ML Interviews
    by /u/xiaohk (Machine Learning) on April 29, 2024 at 5:18 pm

    I'm preparing for an MLE interview and I've noticed that some companies ask candidates to implement models or layers from research papers during their interviews. Could you recommend some NLP research papers that I can practice my implementation skill with PyTorch? I guess the papers used in interviews are challenging and yet manageable within the scope of a 1-hour interview. Thank you! Some classic papers I can think of: multi-head attention BERT GPT-2 submitted by /u/xiaohk [link] [comments]

  • Revolutionizing large language model training with Arcee and AWS Trainium
    by Mark McQuade (AWS Machine Learning Blog) on April 29, 2024 at 3:21 pm

    This is a guest post by Mark McQuade, Malikeh Ehghaghi, and Shamane Siri from Arcee. In recent years, large language models (LLMs) have gained attention for their effectiveness, leading various industries to adapt general LLMs to their data for improved results, making efficient training and hardware availability crucial. At Arcee, we focus primarily on enhancing

  • [D] Advice for Non CS Major in ML
    by /u/Character-Capital-70 (Machine Learning) on April 29, 2024 at 3:12 pm

    Does anyone have advice for people without a CS background breaking into the ML industry? Ive been doing undergrad ML research for about 3 years now (from a cogsci perspective) but I find the MLOps/coding/implementation of it difficult and confusing, (especially when classes and objects are introduced). I have no problem copying and pasting different parts of code from keras/tf and using ChatGPT for help to get my ML model to work properly. But if I need to build something from scratch, or for instance when I need to define custom callbacks or other programming heavy tasks I feel overwhelmed. Additionally, I know ML roles require technical/coding interviews which I am not confident in my ability, I wouldn’t be able to solve them without external help due to my overreliance on stuff like ChatGPT (which surprisingly works amazingly well). Is anyone in a similar situation or has advice for how to best learn to code better? submitted by /u/Character-Capital-70 [link] [comments]

  • [D] How can attention mechanisms retrieve meaningful information over long distances when using RoPE or ALiBi?
    by /u/kiockete (Machine Learning) on April 29, 2024 at 2:30 pm

    If both RoPE and ALiBi work under the assumption that we should assign increasingly lower scores the further apart two tokens are, wouldn't the score be so penalized at some point that even if there is an interesting fact 1 million tokens away, we couldn't retrieve it because the positional encoding would force it to have such a low score? submitted by /u/kiockete [link] [comments]

  • [D] Do Lead's in an AI/DS/ML team always have PhDs, is it a requirement?
    by /u/Rajivrocks (Machine Learning) on April 29, 2024 at 1:49 pm

    Hello all, I am a uni student for a masters in AI. During my bachelors I did my thesis at a company and the lead AI had a PhD in Evolutionary algo's. I had a guest lecture from a lead DS last week from a multi billion dollar online marketplace and he also has a PhD. these are a few examples of Leads with PhDs that I've seen. So this poses the question, is it necessary to have a PhD to become a Lead for an AI/ML/DS team? I am just curious, I don't know if that would be something I'd like to aspire to do, senior is also fine in the end. But I see it so many times, I haven't seen the opposite, as in a Lead with only a Masters degree. I am not seeking any career advice, I am not planning to get a PhD at all, I just observe this a lot so I'm curious. Any thoughts? submitted by /u/Rajivrocks [link] [comments]

  • [D] Sequential model bad at predicting my own handwriting?
    by /u/blackrat13 (Machine Learning) on April 29, 2024 at 12:04 pm

    Hi everyone, I've created a sequential model in tensorflow to predict handwritten digits with an accuracy of 0.96. On the dataset provided by tf it performed very well, but if i try to predict on my own handwritten digits it always outputs 8. I have converted the photo to grayscale, resized it to 28 x 28, converted to an array, and reshaped it. Any thoughts on why its not working? Thanks! https://colab.research.google.com/drive/1iv7VlPnWxHkbxZCuWucxd3mcNP23UbtG?usp=sharing submitted by /u/blackrat13 [link] [comments]

  • [D] Correct me if I'm wrong, use KL divergence for NLP, and MMD for CV. Both are measuring the similarity/distance of two distribution
    by /u/VoiceBeer (Machine Learning) on April 29, 2024 at 9:44 am

    I'm doing instance selection now, and after a quick research, I found fewer works are using MMD rather than KL divergence. Is there a preference for choosing a distance metric in these two fields? What is the reason behind this? THX submitted by /u/VoiceBeer [link] [comments]

  • [D] How non-Python apps are integrating AI currently? What works ?
    by /u/Emergency-Director53 (Machine Learning) on April 29, 2024 at 6:08 am

    I think the straight forward thing to do is to develop a Python microservice and integrate with existing code but I am also hearing developers just integrating AI in their native language like Java, .Net as it's just comfortable for their existing dev team. submitted by /u/Emergency-Director53 [link] [comments]

  • [R] New Teleoperation Tool with VisionPro
    by /u/XiaolongWang (Machine Learning) on April 29, 2024 at 5:40 am

    submitted by /u/XiaolongWang [link] [comments]

  • [R] Dynamic Gaussians Mesh
    by /u/XiaolongWang (Machine Learning) on April 29, 2024 at 5:29 am

    submitted by /u/XiaolongWang [link] [comments]

  • [D] ICML 2024 results
    by /u/South-Conference-395 (Machine Learning) on April 29, 2024 at 12:57 am

    Hi everyone, The ICML decisions are coming up soon! I'm creating a post for everyone interested in sharing: thoughts about the results/ review process interesting stats and trends in accepted papers discussions about current research trends brainstorming on novel works to be presented at the conference (which one is your favorite ? :)) (for those attending) a casual meetup for ICML in Vienna ! best of luck everyone! submitted by /u/South-Conference-395 [link] [comments]

  • You need everything other than ML to win a ML hackathon [D]
    by /u/ade17_in (Machine Learning) on April 28, 2024 at 9:56 pm

    Basically a rant on condition of offline hackathons hosted my big MNCs and institues. Tired of participating in hackathons aimed to "develope cutting edge solution" and end up losing to a guy who have never studied machine learning but expert in "bussiness informatics" and really good while pitching the solution within given time limit. How can a sane mind who worked on idea, a prototype and a model for 2-3 days non-stop only gets to talk about it just for 3-5 minutes? I've literally seen people cloning github repos somewhat related to the problem statement and sell it like a some kind of state of the art product. I agree that this skills is more important in industry but then why name those hackathons as "Machine Learning" or "AI" hackathons? Better name it "sell me some trash". Only option for someone really into developing a good product, a working model within limited time constraints and someone who loves competing (like me) is to participate online or in "data" competition. submitted by /u/ade17_in [link] [comments]

  • [D] Why isn't RETRO mainstream / state-of-the-art within LLMs?
    by /u/whitetwentyset (Machine Learning) on April 28, 2024 at 7:58 pm

    In 2021, Deepmind published Improving language models by retrieving from trillions of tokens and introduced a Retrieval-Enhanced Transformer (RETRO). Whereas RAG clasically involves supplementing input tokens at inference time by injecting relevant documents into context, RETRO can access related embeddings from an external database during both training and inference. The goal was to decouple reasoning and knowledge: by allowing as-needed lookup, the model can be freed from having to memorize all facts within its weights and instead reallocate energy toward more impactful computations. The results were pretty spectacular: RETRO achieved GPT-3-comparable performance with 25x fewer parameters, and is theoretically without knowledge cutoffs (just add new information to the retrieval DB!). And yet: today, AFAICT, most major models don't incorporate RETRO. LLaMA and Mistral certainly don't, and I don't get the sense that GPT or Claude do either (the only possible exception is Gemini, based on the fact that much of the RETRO team is now part of the Gemini team and that it is both faster and more real-timey in my experience). Moreover, despite that RAG has been hot and that one might argue MoE enables it, explicitly decoupling reasoning and knowledge has been relatively quiet as a research vector. Does anyone have a confident explanation of why this is so? I feel like RETRO's this great efficient frontier advancement sitting in plain sight just waiting for widespread adoption, but maybe I'm missing something obvious. submitted by /u/whitetwentyset [link] [comments]

  • [P] I created a cloud provider for affordable & easy GPU access
    by /u/ojasaar (Machine Learning) on April 28, 2024 at 7:21 pm

    Hello r/MachineLearning! I’m thrilled to introduce Backprop GPU Cloud—after three years of hosting GPU servers on public marketplaces I decided to build my own cloud to offer a better service. I've focused on speed, price, and reliability: - Instances are created in <60s with Jupyter pre-installed. - The pricing is reasonable with no hidden fees on storage or bandwidth. - The RTX 3090 instances are hosted in a tier III data center with 10 Gbps networking. - You get a virtual machine with full root access and a dedicated IPv4 address. If you're a student or a researcher, I'm happy to give you 10 hours of free credit. Just sign up and shoot me a message. I'm looking to add more features and additional instance types. All feedback would help a ton! submitted by /u/ojasaar [link] [comments]

  • [Research] A visual deep dive into Uber's machine learning solution for predicting ETAs.
    by /u/ml_a_day (Machine Learning) on April 28, 2024 at 6:18 pm

    TL;DR: Uber follows a 2-layer approach. They combine traditional graph algorithms like Dijkstra with learned embeddings and a lightweight self-attention neural network to reliably predict estimated time of arrival or ETA. How Uber uses ML to ETAs (and solve a billion dollar problem) https://preview.redd.it/2ovttr82i9xc1.png?width=1358&format=png&auto=webp&s=51b12261bf98f529fd0e9b33daf6362b727f4580 submitted by /u/ml_a_day [link] [comments]

  • [R] Categorical Deep Learning: An Algebraic Theory of Architectures
    by /u/SeawaterFlows (Machine Learning) on April 28, 2024 at 5:59 pm

    Paper: https://arxiv.org/abs/2402.15332 Project page: https://categoricaldeeplearning.com/ Abstract: We present our position on the elusive quest for a general-purpose framework for specifying and studying deep learning architectures. Our opinion is that the key attempts made so far lack a coherent bridge between specifying constraints which models must satisfy and specifying their implementations. Focusing on building a such a bridge, we propose to apply category theory -- precisely, the universal algebra of monads valued in a 2-category of parametric maps -- as a single theory elegantly subsuming both of these flavours of neural network design. To defend our position, we show how this theory recovers constraints induced by geometric deep learning, as well as implementations of many architectures drawn from the diverse landscape of neural networks, such as RNNs. We also illustrate how the theory naturally encodes many standard constructs in computer science and automata theory. submitted by /u/SeawaterFlows [link] [comments]

  • [D] How would you diagnose these spikes in the training loss?
    by /u/NumberGenerator (Machine Learning) on April 28, 2024 at 11:44 am

    submitted by /u/NumberGenerator [link] [comments]

  • "transformers can use meaningless filler tokens (e.g., '......') in place of a chain of thought" - Let's Think Dot by Dot [P]
    by /u/Agitated_Space_672 (Machine Learning) on April 28, 2024 at 9:59 am

    https://arxiv.org/abs/2404.15758 From the abstract We show that transformers can use meaningless filler tokens (e.g., '......') in place of a chain of thought to solve two hard algorithmic tasks they could not solve when responding without intermediate tokens. However, we find empirically that learning to use filler tokens is difficult and requires specific, dense supervision to converge submitted by /u/Agitated_Space_672 [link] [comments]

  • Databricks DBRX is now available in Amazon SageMaker JumpStart
    by Shikhar Kwatra (AWS Machine Learning Blog) on April 26, 2024 at 7:52 pm

    Today, we are excited to announce that the DBRX model, an open, general-purpose large language model (LLM) developed by Databricks, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. The DBRX LLM employs a fine-grained mixture-of-experts (MoE) architecture, pre-trained on 12 trillion tokens of carefully curated data and

  • Knowledge Bases in Amazon Bedrock now simplifies asking questions on a single document
    by Suman Debnath (AWS Machine Learning Blog) on April 26, 2024 at 7:12 pm

    At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). In previous posts, we covered new capabilities like hybrid search support, metadata filtering

  • Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint
    by Sanjay Tiwary (AWS Machine Learning Blog) on April 25, 2024 at 5:03 pm

    Speaker diarization, an essential process in audio analysis, segments an audio file based on speaker identity. This post delves into integrating Hugging Face’s PyAnnote for speaker diarization with Amazon SageMaker asynchronous endpoints. We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud.

  • Evaluate the text summarization capabilities of LLMs for enhanced decision-making on AWS
    by Dinesh Subramani (AWS Machine Learning Blog) on April 25, 2024 at 4:25 pm

    Organizations across industries are using automatic text summarization to more efficiently handle vast amounts of information and make better decisions. In the financial sector, investment banks condense earnings reports down to key takeaways to rapidly analyze quarterly performance. Media companies use summarization to monitor news and social media so journalists can quickly write stories on

  • Enhance conversational AI with advanced routing techniques with Amazon Bedrock
    by Ameer Hakme (AWS Machine Learning Blog) on April 24, 2024 at 4:30 pm

    Conversational artificial intelligence (AI) assistants are engineered to provide precise, real-time responses through intelligent routing of queries to the most suitable AI functions. With AWS generative AI services like Amazon Bedrock, developers can create systems that expertly manage and respond to user requests. Amazon Bedrock is a fully managed service that offers a choice of

  • Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering
    by Yunfei Bai (AWS Machine Learning Blog) on April 24, 2024 at 4:27 pm

    The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The team navigates a large volume of documents and locates the right information to make sure the warehouse design meets the highest standards. In the post A generative AI-powered solution on Amazon SageMaker to help Amazon EU

  • Improve accuracy of Amazon Rekognition Face Search with user vectors
    by Arik Porat (AWS Machine Learning Blog) on April 24, 2024 at 4:13 pm

    In various industries, such as financial services, telecommunications, and healthcare, customers use a digital identity process, which usually involves several steps to verify end-users during online onboarding or step-up authentication. An example of one step that can be used is face search, which can help determine whether a new end-user’s face matches those associated with

  • Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support
    by Shweta Singh (AWS Machine Learning Blog) on April 23, 2024 at 7:20 pm

    We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode and Docker support. ML model development often involves slow iteration cycles as developers switch between coding, training, and deployment. Each step requires waiting for remote compute resources to start up, which

  • Significant new capabilities make it easier to use Amazon Bedrock to build and scale generative AI applications – and achieve impressive results
    by Swami Sivasubramanian (AWS Machine Learning Blog) on April 23, 2024 at 11:50 am

    We introduced Amazon Bedrock to the world a little over a year ago, delivering an entirely new way to build generative artificial intelligence (AI) applications. With the broadest selection of first- and third-party foundation models (FMs) as well as user-friendly capabilities, Amazon Bedrock is the fastest and easiest way to build and scale secure generative

  • Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock
    by Mani Khanuja (AWS Machine Learning Blog) on April 23, 2024 at 11:40 am

    This post explores the new enterprise-grade features for Knowledge Bases on Amazon Bedrock and how they align with the AWS Well-Architected Framework. With Knowledge Bases for Amazon Bedrock, you can quickly build applications using Retrieval Augmented Generation (RAG) for use cases like question answering, contextual chatbots, and personalized search.

  • Integrate HyperPod clusters with Active Directory for seamless multi-user login
    by Tomonori Shimomura (AWS Machine Learning Blog) on April 22, 2024 at 5:50 pm

    Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. With SageMaker HyperPod, you can train FMs for weeks and months without disruption. Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists,

  • The executive’s guide to generative AI for sustainability
    by Wafae Bakkali (AWS Machine Learning Blog) on April 22, 2024 at 5:40 pm

    Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. A Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organization’s investment in sustainability over the next years. This post serves as a starting point for any executive seeking to navigate the intersection of generative

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on April 21, 2024 at 3:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

  • Introducing automatic training for solutions in Amazon Personalize
    by Ba'Carri Johnson (AWS Machine Learning Blog) on April 20, 2024 at 12:38 am

    Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure recommendations align with users’ evolving behaviors and preferences. As data patterns and trends change over time, retraining the solution with the latest relevant data enables the model to learn and adapt,

  • Use Kubernetes Operators for new inference capabilities in Amazon SageMaker that reduce LLM deployment costs by 50% on average
    by Rajesh Ramchander (AWS Machine Learning Blog) on April 19, 2024 at 4:55 pm

    We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues

  • Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock – Part 2
    by Archana Inapudi (AWS Machine Learning Blog) on April 19, 2024 at 3:15 pm

    In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a slide deck into embeddings. We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) model to generate text responses to user questions based on

  • Scale AI training and inference for drug discovery through Amazon EKS and Karpenter
    by Matthew Welborn (AWS Machine Learning Blog) on April 19, 2024 at 3:07 pm

    This is a guest post co-written with the leadership team of Iambic Therapeutics. Iambic Therapeutics is a drug discovery startup with a mission to create innovative AI-driven technologies to bring better medicines to cancer patients, faster. Our advanced generative and predictive artificial intelligence (AI) tools enable us to search the vast space of possible drug

  • Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock
    by Ebbey Thomas (AWS Machine Learning Blog) on April 18, 2024 at 5:57 pm

    As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security configurations, resource provisioning, and account management. It’s particularly beneficial for organizations looking to scale without compromising on governance or control, providing a clear path to a robust and efficient cloud setup. In this post, we show you how to generate customized, compliant IaC scripts for AWS Landing Zone using Amazon Bedrock.

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QNN – Read Breaking News and Trivia to Me

QNN - Read Breaking News and Trivia to Me

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QNN - Read Breaking News and Trivia to Me: QNN is an open source breaking news and trivia app that allows you to get breaking news and elevate your brain with trivia in real time. QNN Aggregates the best news feed around the globe and present it to you cleanly without the clutter.

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