Beating random: What it means to have trained a model

Tic-Tac-Toe the Hard Way

MP3•Pagina principale dell'episodio

22:08

Host Francesca Amiker sits down with directors Joe and Anthony Russo, producer Angela Russo-Otstot, stars Millie Bobby Brown and Chris Pratt, and more to uncover how family was the key to building the emotional core of The Electric State . From the Russos’ own experiences growing up in a large Italian family to the film’s central relationship between Michelle and her robot brother Kid Cosmo, family relationships both on and off of the set were the key to bringing The Electric State to life. Listen to more from Netflix Podcasts . State Secrets: Inside the Making of The Electric State is produced by Netflix and Treefort Media.…

5 anni fa 17:14

For more information about the show, check out pair.withgoogle.com/thehardway/.

You can reach out to the hosts on Twitter: @dweinberger and @tafsiri.

10 episodi

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

Beating random: What it means to have trained a model

Tic-Tac-Toe the Hard Way

published 5 anni fa

MP3•Pagina principale dell'episodio

For more information about the show, check out pair.withgoogle.com/thehardway/.

You can reach out to the hosts on Twitter: @dweinberger and @tafsiri.

10 episodi

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

Tutti gli episodi

1
Lessons learned 33:01

5 years fa33:01

33:01

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 .…

1
Head to Head: The Even Bigger ML Smackdown! 24:26

5 years fa24:26

24:26

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 .…

1
Enter tic-tac-two 21:20

5 years fa21:20

21:20

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 .…

1
Head to Head: the Big ML Smackdown! 25:19

5 years fa25:19

25:19

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 .

1
Give that model a treat! : Reinforcement learning explained 26:04

5 years fa26:04

26:04

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 .…

1
Beating random: What it means to have trained a model 17:14

5 years fa17:14

17:14

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 .…

1
From tic-tac-toe moves to ML model 21:37

5 years fa21:37

21:37

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 .…

1
What does a tic-tac-toe board look like to machine learning? 23:26

5 years fa23:26

23:26

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 .…

1
Howdy, and the myth of “pouring in data” 22:01

5 years fa22:01

22:01

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 .…

1
Introducing Tic-Tac-Toe the Hard Way 2:09

5 years fa2:09