Dale’s Blog → https://goo.gle/3xOeWoK Classify text with BERT → https://goo.gle/3AUB431 Over the past five years, Transformers, a neural network architecture, have completely transformed state-of-the-art natural language processing. Want to translate text with machine learning? Curious how an ML model could write a poem or an op ed? Transformers can do it all. In this episode of Making with ML, Dale Markowitz explains what transformers are, how they work, and why they’re so impactful. Watch to learn how you can start using transformers in your app! Chapters: 0:00 – Intro 0:51 – What are transformers? 3:18 – How do transformers work? 7:41 – How are transformers used? 8:35 – Getting started with transformers Watch more episodes of Making with Machine Learning → https://goo.gle/2YysJRY Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #MakingwithMachineLearning #MakingwithML product: Cloud – General; fullname: Dale Markowitz; re_ty: Publish;
What are Network Models? What is Client Server Model ? What is Peer to Peer Model? What is Hybrid Model?
Professor Hima Lakkaraju of Harvard University joined us on April 11, 2022, for “Does Model Understanding Improve Human Decision Making?” Abstract As machine learning (ML) models are increasingly being employed to make consequential decisions in high-stakes settings such as finance, healthcare, and hiring, it becomes important to ensure that these models are actually beneficial to human decision makers. To this end, recent research in ML has focused on developing techniques which aim to explain complex models to domain experts/decision makers so that they can determine if, when, and how much to rely on the predictions of these models. In this talk, I will give a brief overview of the state-of-the-art in explaining ML models, and then present some of our recent research on understanding the impact of explaining the rationale behind model predictions to decision makers. More specifically, I will discuss two user studies that we carried out with domain experts in healthcare (e.g., doctors) and hiring (e.g., recruiters) settings where we analyzed the impact of explaining the rationale behind model predictions on the accuracy and the discriminatory biases in the decision making process.
Microsoft and Nvidia have been working hard to finally create an Artificial Intelligence Model which surpasses and beats OpenAI’s GPT3 with more than double the parameter count and almost reaching the amazing and intelligent amount of 1 Trillion Parameter models. Unless OpenAI comes out with GPT4, it seems like the Megatron-Turing NLP AI Model is to be the best and smartest Artificial Intelligence of 2021 which the most abilities of any Natural Language Processing AI ever. It’s also much easier to train than GPT3. It requires much less hardware and maybe with the upcoming Nvidia Lovelace GPU’s, it’ll be even easier to run for regular consumers. —– Every day is a day closer to the Technological Singularity. Experience Robots learning to walk & think, humans flying to Mars and us finally merging with technology itself. And as all of that happens, we at AI News cover the absolute cutting edge best technology inventions of Humanity. If you enjoyed this video, please consider rating this video and subscribing to our channel for more frequent uploads. Thank you! 🙂 —– TIMESTAMPS: 00:00 GPT-3 has been beaten 02:09 How Transformers work 03:53 What’s new about this AI Model? 05:45 The Future of Artificial Intelligence 09:32 Last Words —– #ai #agi #microsoft
This talk will introduce participants to the theory and practice of machine learning in production. The talk will begin with an intro on machine learning models and data science systems and then discuss data pipelines, containerization, real-time vs. batch processing, change management and versioning. As part of this talk, an audience will learn more about: • How data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models. • How organizations can accelerate machine learning from research to production while preserving the flexibility and agility of data scientists and modern business use cases demand. A small demo will showcase how to rapidly build, train, and deploy machine learning models in R, python, and Spark, and continue with a discussion of API services, RESTful wrappers/Docker, PMML/PFA, Onyx, SQLServer embedded models, and lambda functions.
It’s time to reveal the magician’s secrets behind deploying machine learning models! In this tutorial, I go through an example machine learning deployment scenario using Google Cloud and an image recognition app called Food Vision 🍔👁. Get all the code on GitHub – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial Slides – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial/blob/main/CS329s-deploying-ml-models-tutorial.pdf Full CS329s syllabus – https://stanford-cs329s.github.io/index.html Learn ML (my beginner-friendly ML course) – https://dbourke.link/mlcourse Connect elsewhere: Web – https://www.mrdbourke.com Get email updates on my work – https://www.mrdbourke.com/newsletter Timestamps: 0:00 – Intro/hello 1:42 – Presentation start (what we’re going to cover) 6:00 – Food Vision 🍔👁 (the app we’re building) recipe 11:16 – The end goal we’re working towards (data flywheel) 13:07 – The data flywheel: the holy grail of ML apps 14:57 – Tesla’s data flywheel 17:02 – Food Vision’s data flywheel 18:24 – Deploying a model on the cloud outline 21:14 – Steps we’re going to go through to deploy our app 27:06 – Question: “How do you identify hard samples in your data?” 37:53 – Creating a bucket on Google Storage 45:51 – Uploading to Google Storage from Google Colab 48:02 – Deploying a model to AI Platform 52:50 – Creating an AI Platform Prediction version 58:10 – Creating a Service Account to access our model on Google Cloud 1:02:32 – Authenticating our app with our private Service Account key 1:09:19 – What happens when we run make gcloud-deploy 1:11:27 – Problems you’ll run into when deploying your models 1:20:12 – Extensions you could perform on this tutorial 1:20:49 – Part 2 [More]
🔥Edureka AWS Training: https://www.edureka.co/aws-certification-training This Edureka video on “Deploy an ML Model using Amazon Sagemaker” discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker. These are the topics covered in the AWS Machine Learning Tutorial video: 00:00:00 Introduction 00:01:14 What is Amazon Sagemaker? 00:04:21 Create your AWS Account 00:06:46 Create your First Notebook Instance 00:17:39 Train your Model on AWS 00:24:37 Deploy your Model on AWS 00:26:33 Evaluate your Model on AWS 00:29:03 AWS SageMaker Case Study: Grammarly 🔹Check Edureka’s complete DevOps playlist here: http://goo.gl/O2vo13 🔹Check Edureka’s Blog playlist here: https://bit.ly/3gfNuZr ——————————————————————————————– 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?country=in Meetup: https://www.meetup.com/edureka/ #Edureka #DeployAnMlModelUsingAmazonSagemaker #AWSTutorial #AWSCertification #AWSTraining #AWSMachineLearning #AWSMLDeployment #MachineLearningOnCloud #CloudComputing #AWS ——————————————————————————————– How it Works? 1. This is a 5 Week Instructor led Online Course. 2. Course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project 3. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 4. You will get Lifetime Access to the recordings in the LMS. 5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! – – – – – – – – – – – – – – About [More]
This talk will focus on creating a production machine learning pipeline using TFX. Using TFX developers can implement machine learning pipelines capable of processing large datasets for both modeling and inference. In addition to data wrangling and feature engineering over large datasets, TFX enables detailed model analysis and versioning. The talk will focus on implementing a TFX pipeline and a discussion of current topics in model understanding. Watch more #io19 here: Machine Learning at Google I/O 2019 Playlist → https://goo.gle/2URpjol TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions Learn more on the I/O Website → https://google.com/io Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1 Get started at → https://www.tensorflow.org/ Speaker(s): Kevin Haas , Tulsee Doshi , Konstantinos Katsiapis T02F52 event: Google I/O 2019; re_ty: Publish; product: TensorFlow – TensorFlow Extended; fullname: Tulsee Doshi;
Hello, today at BizzUP we have discussed the Impact Of Artificial Intelligence on Business. The future Business model will have many changes and AI will play a major role in it. This is a part of Industry 4.0. But it will have an impact on service businesses also. I hope you like it! Watch ” What is Industry 4.0? “: https://youtu.be/rl2309yUalI Text “Mutual Fund” on WhatsApp no.: +91-7028585111 and Top 10 People will get our E-Book for Free! Do subscribe to our channel if you like our content. 🙏 Music Copyright: Friendship by Declan DP https://soundcloud.com/declandp Licensing Agreement: http://declandp.info/music-licensing Free Download / Stream: https://bit.ly/friendship-declan-dp Music promoted by Audio Library https://youtu.be/OJYMBfTj3OI Social media: Instagram: https://www.instagram.com/bizzupofficial/ Twitter: https://twitter.com/up_bizz Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational, or personal use tips the balance in favor of fair use. this video with many people you can and share this valuable knowledge with them. #Impactofartificialintelligenceonbusiness #futurebusinessmodel #bizzup #ai #aitechnology #industry4.0 #robots #analytics #automation #datasecurity #machinelearning #futuretechnology #techonology #iot
architecture peer to peer model in distributed systems
distributed computing system model
Welcome to Zero to Hero for Natural Language Processing using TensorFlow! If you’re not an expert on AI or ML, don’t worry — we’re taking the concepts of NLP and teaching them from first principles with our host Laurence Moroney (@lmoroney). In the last couple of episodes you saw how to tokenize text into numeric values and how to use tools in TensorFlow to regularize and pad that text. Now that we’ve gotten the preprocessing out of the way, we can next look at how to build a classifier to recognize sentiment in text. Colab → https://goo.gle/tfw-sarcembed GitHub → https://goo.gle/2PH90ea NLP Zero to Hero playlist → https://goo.gle/nlp-z2h Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow
Causality and Increasing model Reliability: Learning Models that are Safe and Robust to Dataset Shifts Suchi Saria directs the Machine Learning and Healthcare Lab at Johns Hopkins University and is the founding research director of the Malone Center for Engineering in Healthcare. She is interested in enabling new classes of diagnostic and treatment planning techniques for healthcare—tools that use statistical machine learning techniques to tease out subtle information from “messy” observational datasets and provide reliable inferences for individualizing care decisions. —– The Summer School of Machine Learning at Skoltech (SMILES) is an online one-week intensive course about modern statistical machine learning methods. It aims at bringing together the Machine Learning community from the CIS, Central Asia, and the Caucasus regions. SMILES presents topics that are at the core of machine learning research, from fundamentals to the state-of-the-art.
Visit us at http://www.siemens-healthineers.com/RSNA Artificial intelligence (AI) is transforming care delivery and expanding precision medicine. Siemens Healthineers has served as a pioneer in AI development for more than 20 years, and new deep learning technology now enables us to automate complex diagnostics and support optimal treatment. #RSNA18 #Radiology #DigitalHealth
The Academic Research Summit, co-organized by Microsoft Research and the Association for Computing Machinery, is a forum to foster meaningful discussion among the Indian computer science research community and raise the bar on research efforts. The third edition of Academic Research Summit was held at the International Institute of Information Technology (IIIT) Hyderabad on the 24th and 25th of January 2018. The agenda included keynotes and talks from distinguished researchers from India and across the world. The summit also had sessions focused on specific topics related to the theme of Artificial Intelligence: A Future with AI. More talks at: https://www.youtube.com/playlist?list=PLD7HFcN7LXRcBpyp34moH_-dVJTX7bXxW
In this video, I explain the client server model. I define what a client is, both a client machine and a client program. Then, I talk about servers. After that, I explain the client server architecture. I also talk about the peer to peer model. REST demystified video: https://youtu.be/FOZtRzY5x8E Software and Web Application Architecture demystified: https://youtu.be/lTkL1oIMiaU Follow me on my new WebDev Cave Facebook page: https://www.facebook.com/webdevcave/
Model Targets represent the most recent advancement in Vuforia object recognition technology, allowing for the detection and tracking of objects from 3D models. View the original here: https://youtu.be/y70yStPCBHA
ADEL Biometric Fingerprint Door Lock – New model. Music by Kevin Macleod Find it on Amazon: http://goo.gl/WKmj4z Disclaimer: The JRESHOW receives free products to create these videos from companies, start-ups, and distributers all around the world. These videos created are intended to be entertaining, descriptive, and unbiased to the product or service of topic. In addition, we are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and affiliated sites.