Tic-Tac-Toe The Hard Way podcast

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Innhold levert av Lucas Dixon and People + AI Research. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Lucas Dixon and People + AI Research eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

This Is Woman's Work with Nicole Kalil

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How To Pitch Yourself (And Get A Yes) | 300 27:52

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We made it— 300 episodes of This Is Woman’s Work ! And we’re marking this milestone by giving you something that could seriously change the game in your business or career: the skill of pitching yourself effectively. Whether you’re dreaming of being a podcast guest, landing a speaking gig, signing a client, or just asking for what you want with confidence—you’re already pitching yourself, every day. But are you doing it well? In this milestone episode, Nicole breaks down exactly how to pitch yourself to be a podcast guest … and actually hear “yes.” With hundreds of pitches landing in her inbox each month, she shares what makes a guest stand out (or get deleted), the biggest mistakes people make, and why podcast guesting is still one of the most powerful ways to grow your reach, authority, and influence. In This Episode, We Cover: ✅ Why we all need to pitch ourselves—and how to do it without feeling gross ✅ The step-by-step process for landing guest spots on podcasts (and more) ✅ A breakdown of the 3 podcast levels: Practice, Peer, and A-List—and how to approach each ✅ The must-haves of a successful podcast pitch (including real examples) ✅ How to craft a pitch that gets read, gets remembered, and gets results Whether you’re new to pitching or want to level up your game, this episode gives you the exact strategy Nicole and her team use to land guest spots on dozens of podcasts every year. Because your voice deserves to be heard. And the world needs what only you can bring. 🎁 Get the FREE Podcast Pitch Checklist + Additional Information on your Practice Group, Peer Group, and A-List Group Strategies: https://nicolekalil.com/podcast 📥 Download The Podcast Pitch Checklist Here Related Podcast Episodes: Shameless and Strategic: How to Brag About Yourself with Tiffany Houser | 298 How To Write & Publish A Book with Michelle Savage | 279 How To Land Your TED Talk and Skyrocket Your Personal Brand with Ashley Stahl | 250 Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…

Tic-Tac-Toe the Hard Way

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A writer and a software engineer from Google's People + AI Research team explore the human choices that shape machine learning systems by building competing tic-tac-toe agents.

10 episoder

#Tech #Podcasting Education #Rebecca Salois #People AI Research #Machine Learning #Human Centered #Reinforcement Learning #Supervised Learning #Tic-tac-toe #Games #Google

Tic-Tac-Toe the Hard Way

updated for 5 år siden

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Serier hjem•Feed

A writer and a software engineer from Google's People + AI Research team explore the human choices that shape machine learning systems by building competing tic-tac-toe agents.

10 episoder

#Tech #Podcasting Education #Rebecca Salois #People AI Research #Machine Learning #Human Centered #Reinforcement Learning #Supervised Learning #Tic-tac-toe #Games #Google

כל הפרקים

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Lessons learned 33:01

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What have we learned about machine learning and the human decisions that shape it? And is machine learning perhaps changing our minds about how the world outside of machine learning — also known as the world — works? For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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Head to Head: The Even Bigger ML Smackdown! 24:26

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Yannick and David’s systems play against each other in 500 games. Who’s going to win? And what can we learn about how the ML may be working by thinking about the results? See the agents play each other in Tic-Tac-Two ! For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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Enter tic-tac-two 21:20

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David’s variant of tic-tac-toe that we’re calling tic-tac-two is only slightly different but turns out to be far more complex. This requires rethinking what the ML system will need in order to learn how to play, and how to represent that data. For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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Head to Head: the Big ML Smackdown! 25:19

for 5 years siden25:19

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David and Yannick’s tic-tac-toe ML agents face-off against each other in tic-tac-toe! See the agents play each other ! For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .

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Give that model a treat! : Reinforcement learning explained 26:04

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Switching gears, we focus on how Yannick’s been training his model using reinforcement learning. He explains the differences from David’s supervised learning approach. We find out how his system performs against a player that makes random tic-tac-toe moves. Resources: Deep Learning for JavaScript book Playing Atari with Deep Reinforcement Learning Two Minute Papers episode on Atari DQN For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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Beating random: What it means to have trained a model 17:14

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David did it! He trained a machine learning model to play tic-tac-toe! (Well, with lots of help from Yannick.) How did the whole training experience go? How do you tell how training went? How did his model do against a player that makes random tic-tac-toe moves? For more information about the show, check out pair.withgoogle.com/thehardway/ . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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From tic-tac-toe moves to ML model 21:37

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Once we have the data we need—thousands of sample games--how do we turn it into something the ML can train itself on? That means understanding how training works, and what a model is. Resources: See a definition of one-hot encoding For more information about the show, check out pair.withgoogle.com/thehardway . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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What does a tic-tac-toe board look like to machine learning? 23:26

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How should David represent the data needed to train his machine learning system? What does a tic-tac-toe board “look” like to ML? Should he train it on games or on individual boards? How does this decision affect how and how well the machine will learn to play? Plus, an intro to reinforcement learning, the approach Yannick will be taking. For more information about the show, check out pair.withgoogle.com/thehardway . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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Howdy, and the myth of “pouring in data” 22:01

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Welcome to the podcast! We’re Yannick and David, a software engineer and a non-technical writer. Over the next 9 episodes we’re going to use two different approaches to build machine learning systems that play two versions of tic-tac-toe. Building a machine learning app requires humans making a lot of decisions. We start by agreeing that David will use a “supervised learning” approach while Yannick will go with “reinforcement learning.” For more information about the show, check out pair.withgoogle.com/thehardway . You can reach out to the hosts on Twitter: @dweinberger and @tafsiri .…

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Introducing Tic-Tac-Toe the Hard Way 2:09

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Introducing the podcast where a writer and a software engineer explore the human choices that shape machine learning systems by building competing tic-tac-toe agents. Brought to you by Google's People + AI Research team. More at: pair.withgoogle.com/thehardway

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