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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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Practical AI Practical AI #59

Flying high with AI drone racing at AlphaPilot

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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Machine Learning github.com

The fastest way to build custom ML tools

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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TensorFlow github.com

TensorFlow 2.0 focuses on simplicity and ease of use

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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Practical AI Practical AI #50

Celebrating episode 50 and the neural net!

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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Practical AI Practical AI #49

Exposing the deception of DeepFakes

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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Practical AI Practical AI #47

GANs, RL, and transfer learning oh my!

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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Practical AI Practical AI #45

How to get plugged into the AI community

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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Practical AI Practical AI #44

AI adoption in the enterprise

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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Victor Zhou victorzhou.com

Random Forests for complete beginners

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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Practical AI Practical AI #42

TensorFlow Dev Summit 2019

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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Netflix Technology Blog Icon Netflix Technology Blog

Python at Netflix

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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Practical AI Practical AI #40

Deep Reinforcement Learning

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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Practical AI Practical AI #39

Making the world a better place at the AI for Good Foundation

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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Hamel Husain towardsdatascience.com

How to automate tasks on GitHub with machine learning for fun and profit

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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