Computerphile: Deep Learning
Google, Facebook & Amazon all use deep learning methods, but how does it work? Research fellow & Deep Learning expert Brais Martinez explains.
AlphaZero’s bishop pair proves too difficult for Stockfish
When AlphaZero, playing black in this game, encounters the Ruy Lopez, it certainly seems to be in favor of the Berlin Defense. Stockfish injects a minor piece imbalance on move 5. How would AlphaZero play with the bishop pair? Stockfish, as early as move 13, opts for a sacrifice that not only nets two pawns, […]
How to Set Up TensorFlow Object Detection on the Raspberry Pi
Learn how to install TensorFlow and set up the TensorFlow Object Detection API on your Raspberry Pi! These instructions will allow you to detect objects in live video streams from your Picamera or USB webcam. Get a Raspberry Pi: https://amzn.to/2Iki3fb Get a Picamera: https://amzn.to/2rKxarh Handy Picamera + Pi case: https://amzn.to/2LxaUed If you have questions, I […]
[News] Google’s medical AI was super accurate in a lab. Real life was a different story.
A closer look at a story of how the deployment of AI brings its own challenges and what can go wrong. https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/ Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher YouTube Source for this AI Video
Get started with Google Colaboratory (Coding TensorFlow)
Want to get started with Google Colaboratory? In this episode of Coding TensorFlow, Software Engineer, Jake VanderPlas breaks down exactly what you need to get started with Colab. Colaboratory is a free Jupyter notebook environment that requires no setup, and runs entirely (writing, running, & sharing code) on the Cloud. Watch to learn more, and […]
A friendly introduction to linear algebra for ML (ML Tech Talks)
In this session of Machine Learning Tech Talks, Tai-Danae Bradley, Postdoc at X, the Moonshot Factory, will share a few ideas for linear algebra that appear in the context of Machine Learning. Chapters: 0:00 – Introduction 1:37 – Data Representations 15:02 – Vector Embeddings 31:52 – Dimensionality Reduction 37:11 – Conclusion Resources: Google Developer’s ML […]
Introducing Google Coral: Building On-Device AI (Google I/O'19)
This session will introduce you to Google Coral, a new platform for on-device AI application development and showcase it’s machine learning acceleration power with TensorFlow demos. Coral offers the tools to bring private, fast, and efficient neural network acceleration right onto your device and enables you to grow ideas of AI application from prototype to […]
Federated Learning: Machine Learning on Decentralized Data (Google I/O'19)
Meet federated learning: a technology for training and evaluating machine learning models across a fleet of devices (e.g. Android phones), orchestrated by a central server, without sensitive training data leaving any user’s device. Learn how this privacy-preserving technology is deployed in production in Google products and how TensorFlow Federated can enable researchers and pioneers to […]
AI for Mobile and IoT Devices: TensorFlow Lite (Google I/O'19)
Imagine building an app that identifies products in real time with your camera or one that responds to voice commands instantly. In this session, you’ll learn how to build AI into any device using TensorFlow Lite, and no ML experience is required. You’ll discover a library of pretrained models that are ready to use in […]
Towards the future – DeepMind: The Podcast (Season 1, Episode 7)
Selected as “New and Noteworthy” by Apple Podcasts, the highly-praised, award-nominated first season of “DeepMind: The Podcast” explores the fascinating world of artificial intelligence (AI). Join mathematician and broadcaster Hannah Fry as she meets world-class scientists and thinkers as they explain the foundations of AI, explore some of the challenges the field is wrestling with, […]
TensorFlow from the ground up (ML Tech Talks)
In the next talk in our series, Wolff Dobson will discuss 6 easy pieces on what you need to know for TensorFlow from the ground up (tensors, variables, and gradients without using high level APIs). This talk is designed for those that know the basics of Machine Learning but need an overview on the fundamentals […]
Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes – #505
Today we continue our ICML series joined by Gustavo Malkomes, a research engineer at Intel via their recent acquisition of SigOpt. In our conversation with Gustavo, we explore his paper Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design, which focuses on a novel algorithmic solution for the iterative model search process. […]
Learning Long-Time Dependencies with RNNs w/ Konstantin Rusch – #484
Today we conclude our 2021 ICLR coverage joined by Konstantin Rusch, a Ph.D. Student at ETH Zurich. In our conversation with Konstantin, we explore his recent papers, titled coRNN and uniCORNN respectively, which focus on a novel architecture of recurrent neural networks for learning long-time dependencies. We explore the inspiration he drew from neuroscience when […]
Writing the Playbook for Fair & Ethical Artificial Intelligence & Machine Learning (Google I/O'19)
Learn from Googlers who are working to ensure that a robust framework for ethical AI principles are in place, and that Google’s products do not amplify or propagate unfair bias, stereotyping, or prejudice. Hear about the research they are doing to evolve artificial intelligence towards positive goals: from accountability in the ethical deployment of AI, […]
AI Experiments: Making AI Accessible through Play (TF Dev Summit ‘19)
Learn about recently open-sourced creative tools and projects built on top of Tensorflow.js, and how they are being used by makers, developers, and communities around the world. Speaker: Irene Alvarado, Google Relevant links: AI Experiments: https://experiments.withgoogle.com/collection/ai Teachable Machine: https://experiments.withgoogle.com/teachable-machine Move Mirror: https://experiments.withgoogle.com/move-mirror Creatability: https://experiments.withgoogle.com/collection/creatability See the revamped dev site → https://www.tensorflow.org/ Watch all TensorFlow Dev […]
TWiML x Fast.ai Machine & Deep Learning Study Group – Session 3 – 19 January 2019 – Spring 2019
**SUBSCRIBE AND TURN ON NOTIFICATIONS** **twimlai.com** This video is a recap of our Merged Fast.ai x TWiML Online Machine Learning & Deep Learning Study Group. In this session, we review Model Explainability with LIME. It’s not too late to join the study group. Just follow these simple steps: 1. Head over to twimlai.com/meetup, and sign up […]
Naveen Rao & Scott Apeland Interview – Intel Nervana DevCloud
In this episode, I talk to Naveen Rao, VP and GM of Intel’s AI Products Group, and Scott Apeland, director of Intel’s Developer Network. It’s been a few months since we last spoke to Naveen, so he gives us a quick update on what Intel’s been up to and we discuss his perspective on some […]
How Well Can an AI Learn Physics? ⚛
❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper “Learning to Simulate Complex Physics with Graph Networks” is available here: https://arxiv.org/abs/2002.09405 https://sites.google.com/view/learning-to-simulate/home#h.p_hjnaJ6k8y0wo 🌊 The thesis on fluids is available here: Real time fluid simulation and control using the Navier-Stokes equations MSc thesis (2012) ❤️ Watch these videos in […]
TWiML x Fast.ai Deep Learning Part 2 Study Group – Lesson 2
This is a recording of the Fast.ai Deep Learning Part 2 Study Group on Lesson 8b, the second part of the lesson covering matrix multiplication, forward and backward passes, presented by Joseph Catanzarite. It’s not too late to join the study group. Just follow these simple steps: 1. Head over to twimlai.com/meetup, and sign up […]
TWiML & AI x Fast.ai Machine Learning Study Group – Session 11 – December 16, 2018
**SUBSCRIBE AND TURN ON NOTIFICATIONS** **twimlai.com** This video is a recap of our Fast.ai x TWiML Online Machine Learning Study Group. In this session, we review Lesson 11, Embeddings. It’s not too late to join the study group. Just follow these simple steps: 1. Head over to twimlai.com/meetup, and sign up for the programs you’re […]
OpenAI’s GPT-2 Is Now Available – It Is Wise as a Scholar! 🎓
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers Weights & Biases blog post (the notebook is available too!) – https://www.wandb.com/articles/visualize-xgboost-in-one-line – https://colab.research.google.com/drive/1SPludDkpAPonmdDdRlIJ7d5jB2FwVfZn#scrollTo=kszahG2PBN5R Try GPT-2 yourself here and post your results in the comments if you’ve found anything interesting. https://talktotransformer.com/ Check out this GPT-2 implementation too (thanks Robert Miles […]
SingularityNET: Humanity+ Summit – Day 1
Schedule – https://docs.google.com/spreadsheets/d/1jhNNjWW6gpXIlyNQC3OAJDVCYv5Ss9WvpaFCgjVUEGY/edit#gid=0 On 7-8 July 2020 the Humanity+ Summit will take place. You will be able to watch live via the SingularityNET YouTube channel. For more information, please visit: https://humanityplus.org/ —- SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the world’s global brain with a full-stack AI solution powered by […]
Help Protect the Great Barrier Reef with Machine Learning
We are excited to announce a TensorFlow-sponsored Kaggle challenge to locate and identify harmful crown-of-thorns starfish (COTS), as part of a broader partnership between the Commonwealth Scientific and Industrial Research Organization (CSIRO) and Google, to help protect coral reefs everywhere. Join the challenge today at https://goo.gle/reef Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow Source of […]
Two Minute Papers: Photorealistic Images from Drawings | Two Minute Papers #80
The Two Minute Papers subreddit is available here: https://www.reddit.com/r/twominutepapers/ By using a convolutional neural networks (a powerful deep learning technique), it is now possible to build an application that takes a rough sketch as an input, and fetches photorealistic images from a database. ___________________________________ The paper “The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies” […]