This video contains stepwise implementation for training dataset of “Face Emotion Recognition or Facial Expression Recognition” using Transfer Learning in Tensorflow-Keras API (00:00:00) concepts (00:23:01) installation (00:30:52) implementation (1:15:08) Live Webcame demo
In the coming years, artificial intelligence will be nearly everywhere. AI robots are expected to be able to drive a truck by 2027, work in retail by 2031, write a best-selling book by 2049 and even work as a surgeon come 2053. Accenture’s James Wilson says workers shouldn’t wait in becoming trained in AI, telling CRNtv that employers can play a role in democratizing the technology. “Everyone in sales or in maintenance team should be able to use AI. We are starting to see the development of almost point and click tools, like Microsoft-type tools, where you can go on your desktop or mobile app and launch an AI tool and all of a sudden you are empowered to use regression analysis, cluster analysis or classification and anomaly detection. But that’s something anyone can do,” he said. Wilson is the co-author of Humans and Machines: Reimagining Work in the Age of AI. For more of the interview with Wilson, watch CRNtv’s video.
To learn more, please visit: Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this tech talk, we will introduce you to the concepts of Amazon SageMaker including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment of ML models. With zero setup required, Amazon SageMaker significantly decreases your training time and the overall cost of getting ML models from concept to production. Learning Objectives: – Learn the fundamentals of building, training & deploying machine learning models – Learn how Amazon SageMaker provides managed distributed training for machine learning models with a modular architecture – Learn to quickly and easily build, train & deploy machine learning models using Amazon SageMaker
Monet or Picasso? In this episode, we’ll train our own image classifier, using TensorFlow for Poets. Along the way, I’ll introduce Deep Learning, and add context and background on why the classifier works so well. Here are links to learn more, thanks for watching, and have fun! TensorFlow for Poets Codelab: Google’s Udacity class on Deep Learning: TensorFlow tutorial: Google Research blog on Inception: You can follow me on Twitter at for updates on episodes, and of course – Google Developers. Subscribe to Google Developers: – Subscribe to the brand new Firebase Channel: And here’s our playlist:
In his proposed 2020 budget, U.S. President Donald Trump asked Congress for $8.6 billion more for the construction of a border wall. With that in mind, Vice President Mike Pence visited an advanced Border Patrol Training Center in Harper’s Ferry, W.Va., two days later. Thousands of soon-to-be Border Patrol agents receive training there in high tech scenarios that include a virtual reality room, border wall prototypes and a replica of a vehicle customs entry. Cristina Caicedo Smit reports. Originally published at –
Notebook: Blog post: A quick guide on how you can train your own text generating neural network and generate text with it on your own computer! More about textgenrnn: Twitter: Patreon:
Dr. Andrei Borshchev, CEO at The AnyLogic Company, presents at the GE EDGE & Controls Symposium 2019. Topics with video time links below: – 4:46 What is simulation modeling? What is AnyLogic? – 8:02 Digital Twins. 10:37 Case study: Gas Turbine Fleet – 14:08 Why AI and Simulation? Some AI terminology. How can simulation help AI? – 21:21 Example of Deep Reinforcement Learning using simulation: Traffic Light Control (detailed) – 29:53 Case study: AI trained by simulation model in Ferromagnetic Core Production – 32:26 Conclusion. What are the challenges? AI white paper – Find out more about simulation modeling – #AnyLogic #Simulation #AI #DynamicSimulation #DigitalTwin
Presented by Peter Skomoroch, Co-Founder and CEO, SkipFlag (acquired by Workday) Enterprise and consumer applications increasingly apply machine learning to create conversational interfaces. Adding a conversational UX presents a number of challenges for machine learning practicioners attempting to build intelligent applications. This session will describe some lessons learned from the recent wave of bots and from building SkipFlag, an intelligent knowledge base that integrated with Slack. Should you develop your own algorithms or make use of NLP as a service? How should you plan to include humans in the loop? To what degree do you need to specialize your models for the industry you’re working in? We’ll cover these questions and more.
When consumers experience AI/ML benefit from various sources in our daily life, enterprises are facing challenges when applying similar AI/ML techniques to transform business. In this session, we will share how Workday (Enterprise SaaS company on HCM and FIN) has identified specific business problem for ML to solve, collected enough data to prototype, and deployed the solution as part of Workday Application product available to all Workday customers in less than 18months. We will also share lessons learned from legal, privacy, and security aspect with Human-in-the-loop approach which is a critical part of the enterprise ML product development journey.
In 1997, Garry Kasparov became the first knowledge worker to be surpassed by an intelligent machine—at least that is one way to look at the world chess champion’s famous match loss to the IBM supercomputer Deep Blue. Instead of becoming the cognitive John Henry, Kasparov has spent the past 20 years pursuing his fascination with how humans and increasingly powerful AIs can work together. In this talk, he will also discuss the role business must play in moving AI from the laboratory into the mainstream and how the new generation of machine learning can create knowledge that contributes to real insight and understanding, not merely efficiency. Most of all, Kasparov wants us to be optimistic and ambitious about the reality and potential of intelligent machines, what he calls a self-fulfilling prophecy.
Let’s discuss whether you should train your models locally or in the cloud. I’ll go through several dedicated GPU options, then compare three cloud options; AWS, Google Cloud, and FloydHub. I was not endorsed by anyone for this. Code for this video: Please Subscribe! And like. And comment. That’s what keeps me going. High Budget GPU: Titan XP Medium Budget GPU: Small Budget GPU: Build a Deep Learning machine: More learning resources: Join us in the Wizards Slack channel: And please support me on Patreon: Follow me: Twitter: Facebook: Instagram: Signup for my newsletter for exciting updates in the field of AI: Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Figure Eight’s CTO, Robert Munro, will talk about 20 years of experience combining Human and Machine Intelligence. From simple early interactions in games and search engines to complex modern tasks like autonomous vehicles, medical imaging, agriculture, translation, art, and music, he will cover how AI is increasingly incorporated into our everyday lives.