We’ve mentioned ML/AI in the browser and in JS a bunch on this show, but we haven’t done a deep dive on the subject… until now! Victor Dibia helps us understand why people are interested in porting models to the browser and how people are using the functionality. We discuss TensorFlow.js and some applications built using TensorFlow.js
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Chris and Daniel talk with Keith Lynn, AlphaPilot Program Manager at Lockheed Martin. AlphaPilot is an open innovation challenge, developing artificial intelligence for high-speed racing drones, created through a partnership between Lockheed Martin and The Drone Racing League (DRL). AlphaPilot challenged university teams from around the world to design AI capable of flying a drone without any human intervention or navigational pre-programming. Autonomous drones will race head-to-head through complex, three-dimensional tracks in DRL’s new Artificial Intelligence Robotic Racing (AIRR) Circuit. The winning team could win up to $2 million in prizes. Keith shares the incredible story of how AlphaPilot got started, just prior to its debut race in Orlando, which will be broadcast on NBC Sports.
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Streamlit lets you create apps for your machine learning projects with deciptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser. Coming soon to a Practical AI podcast near you…
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Folks have been talking about TensorFlow 2 for some time now (See Practical AI #42 for one excellent example), but now it’s finally here. The bulleted list: Easy model building with Keras and eager execution. Robust model deployment in production on any platform. Powerful experimentation for research. API simplification by reducing duplication and removing deprecated endpoints. This is a huge release. Check out the highlights list in the changelog to see for yourself.
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We’re talking with Sherol Chen, a machine learning developer, about AI at Google and AutoML methods. Sherol explains how the various AI groups within Google work together and how AutoML fits into that puzzle. She also explains how to get started with AutoML step-by-step (this is “practical” AI after all).
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Ported from David Sandberg’s TensorFlow facenet repo.
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Woo hoo! As we celebrate reaching episode 50, we come full circle to discuss the basics of neural networks. If you are just jumping into AI, then this is a great primer discussion with which to take that leap. Our commitment to making artificial intelligence practical, productive, and accessible to everyone has never been stronger, so we invite you to join us for the next 50 episodes!
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This week we bend reality to expose the deceptions of deepfake videos. We talk about what they are, why they are so dangerous, and what you can do to detect and resist their insidious influence. In a political environment rife with distrust, disinformation, and conspiracy theories, deepfakes are being weaponized and proliferated as the latest form of state-sponsored information warfare. Join us for an episode scarier than your favorite horror movie, because this AI bogeyman is real!
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Never heard of algorithmic differentiation? It’s the tool that “underlies modern machine learning”. I learned that from reading the intro to this handbook, btw. It begins with a calculus-101 style understanding and gradually extends this to build toy implementations of systems similar to PyTorch and TensorFlow.
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Daniel and Chris explore three potentially confusing topics - generative adversarial networks (GANs), deep reinforcement learning (DRL), and transfer learning. Are these types of neural network architectures? Are they something different? How are they used? Well, If you have ever wondered how AI can be creative, wished you understood how robots get their smarts, or were impressed at how some AI practitioners conquer big challenges quickly, then this is your episode!
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A simple guide to what CNNs are, how they work, and how to build one from scratch in Python. This buils on Victor’s original intro to Neural Networks that we linked up previously.
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The latest machine learning research from my friends at Fast Forward Labs. Shiou Lin Sam and Nisha Muktewar teach us what meta-learners are and how they learn.
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This wraps up a pre-trained model for SQLova. Here are some examples using the ‘bridges’ dataset. 👇
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Chris and Daniel take you on a tour of local and global AI events, and discuss how to get the most out of your experiences. From access to experts to developing new industry relationships, learn how to get your foot in the door and make connections that help you grow as an AI practitioner. Then drawing from their own wealth of experience as speakers, they dive into what it takes to give a memorable world-class talk that your audience will love. They break down how to select the topic, write the abstract, put the presentation together, and deliver the narrative with impact!
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At the recent O’Reilly AI Conference in New York City, Chris met up with O’Reilly Chief Data Scientist Ben Lorica, the Program Chair for Strata Data, the AI Conference, and TensorFlow World. O’Reilly’s ‘AI Adoption in the Enterprise’ report had just been released, so naturally Ben and Chris wanted to do a deep dive into enterprise AI adoption to discuss strategy, execution, and implications.
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Can AI help quantum physicists? Can quantum physicists help the AI community? The answers are yes and yes! Dr. Shohini Ghose from Wilfrid Laurier University and Marcus Edwards from the University of Waterloo join us to discuss ML/AI’s impact on physics and quantum computing potential for ML/AI.
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Victor Zhou has been killin’ it lately with these explainers: In my opinion, most Machine Learning tutorials aren’t beginner-friendly enough. Last month, I wrote an introduction to Neural Networks for complete beginners. This post will adopt the same strategy, meaning it again assumes ZERO prior knowledge of machine learning. We’ll learn what Random Forests are and how they work from the ground up.
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This week Daniel and Chris discuss the announcements made recently at TensorFlow Dev Summit 2019. They kick it off with the alpha release of TensorFlow 2.0, which features eager execution and an improved user experience through Keras, which has been integrated into TensorFlow itself. They round out the list with TensorFlow Datasets, TensorFlow Addons, TensorFlow Extended (TFX), and the upcoming inaugural O’Reilly TensorFlow World conference.
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A curated list of applied machine learning and data science notebooks and libraries accross different industries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by awesome-machine-learning.
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From the Netflix Technology Blog on how they’re using Python. As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.
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While attending the NVIDIA GPU Technology Conference in Silicon Valley, Chris met up with Adam Stooke, a speaker and PhD student at UC Berkeley who is doing groundbreaking work in large-scale deep reinforcement learning and robotics. Adam took Chris on a tour of deep reinforcement learning - explaining what it is, how it works, and why it’s one of the hottest technologies in artificial intelligence!
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Longtime listeners know that we’re always advocating for ‘AI for good’, but this week we have taken it to a whole new level. We had the privilege of chatting with James Hodson, Director of the AI for Good Foundation, about ways they have used artificial intelligence to positively-impact the world - from food production to climate change. James inspired us to find our own ways to use AI for good, and we challenge our listeners to get out there and do some good!
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This is an explainer on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets. Hamel Husain writes: In order to show you how to create your own apps, we will walk you through the process of creating a GitHub app that can automatically label issues. Note that all of the code for this app, including the model training steps are located in this GitHub repository. See also: Issue Label Bot
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GIPHY’s head of R&D, Nick Hasty, joins us to discuss their recently released celebrity detector project. He gives us all of the details about that project, but he also tells us about GIPHY’s origins, AI in general at GIPHY, and more!
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A simple explanation of Gini Impurity and how it’s used to train decision trees. In brief: Gini Impurity is the probability of incorrectly classifying a randomly chosen element in the dataset if it were randomly labeled according to the class distribution in the dataset Click through for the full break down and some helpful examples.
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