Practical Simulations for Machine Learning Using
Synthetic Data for AI 1st Edition Paris
Buttfield Addison Mars Buttfield Addison Jon
Manning Tim Nugent download
https://ebookmeta.com/product/practical-simulations-for-machine-
learning-using-synthetic-data-for-ai-1st-edition-paris-buttfield-
addison-mars-buttfield-addison-jon-manning-tim-nugent/
Download full version ebook from https://ebookmeta.com
,Practical
Simulations for
Machine Learning
Using Synthetic Data for AI
Paris and Mars Buttfield-Addison,
Tim Nugent & Jon Manning
,Practical Simulations for Machine Learning
Simulation and synthesis are core parts of the future of AI and
machine learning. Consider: programmers, data scientists, “In times where data
and machine learning engineers can create the brain of a needs are high but
self-driving car without the car. Rather than use information access to data is sparse,
from the real world, you can synthesize artificial data using creating lifelike simulated
simulations to train traditional machine learning models. environments to produce
That’s just the beginning. stronger research and
ML applications is more
With this practical book, you’ll explore the possibilities of relevant than ever.
simulation- and synthesis-based machine learning and AI, Practical Simulations for
concentrating on deep reinforcement learning and imitation Machine Learning is a
learning techniques. AI and ML are increasingly data driven, great entry in this space
and simulations are a powerful, engaging way to unlock their for machine learning
full potential. researchers and Unity
You’ll learn how to: developers alike.”
—Dominic Monn
• Design an approach for solving ML and AI problems using Machine Learning Engineer
simulations with the Unity engine
• Use a game engine to synthesize images for use as training Paris Buttfield-Addison is a game
data designer, computing researcher, legal
• Create simulation environments designed for training deep nerd, and cofounder of game
development studio Secret Lab.
reinforcement learning and imitation learning models
Mars Buttfield-Addison is a computing
• Use and apply efficient general-purpose algorithms for and machine learning researcher at the
simulation-based ML, such as proximal policy optimization University of Tasmania.
• Train a variety of ML models using different approaches Tim Nugent is a mobile app developer,
game designer, and computing researcher.
• Enable ML tools to work with industry-standard game
development tools, using PyTorch, and the Unity ML-Agents Jon Manning is a software engineering
expert in Swift, C#, and Objective-C. As
and Perception Toolkits
cofounder of Secret Lab, he created the
popular Yarn Spinner dialog framework
for games.
DATA Twitter: @oreillymedia
linkedin.com/company/oreilly-media
US $59.99 CAN $74.99 youtube.com/oreillymedia
ISBN: 978-1-492-08992-6
, Practical Simulations for
Machine Learning
Using Synthetic Data for AI
Paris and Mars Buttfield-Addison,
Tim Nugent, and Jon Manning
Beijing Boston Farnham Sebastopol Tokyo
Synthetic Data for AI 1st Edition Paris
Buttfield Addison Mars Buttfield Addison Jon
Manning Tim Nugent download
https://ebookmeta.com/product/practical-simulations-for-machine-
learning-using-synthetic-data-for-ai-1st-edition-paris-buttfield-
addison-mars-buttfield-addison-jon-manning-tim-nugent/
Download full version ebook from https://ebookmeta.com
,Practical
Simulations for
Machine Learning
Using Synthetic Data for AI
Paris and Mars Buttfield-Addison,
Tim Nugent & Jon Manning
,Practical Simulations for Machine Learning
Simulation and synthesis are core parts of the future of AI and
machine learning. Consider: programmers, data scientists, “In times where data
and machine learning engineers can create the brain of a needs are high but
self-driving car without the car. Rather than use information access to data is sparse,
from the real world, you can synthesize artificial data using creating lifelike simulated
simulations to train traditional machine learning models. environments to produce
That’s just the beginning. stronger research and
ML applications is more
With this practical book, you’ll explore the possibilities of relevant than ever.
simulation- and synthesis-based machine learning and AI, Practical Simulations for
concentrating on deep reinforcement learning and imitation Machine Learning is a
learning techniques. AI and ML are increasingly data driven, great entry in this space
and simulations are a powerful, engaging way to unlock their for machine learning
full potential. researchers and Unity
You’ll learn how to: developers alike.”
—Dominic Monn
• Design an approach for solving ML and AI problems using Machine Learning Engineer
simulations with the Unity engine
• Use a game engine to synthesize images for use as training Paris Buttfield-Addison is a game
data designer, computing researcher, legal
• Create simulation environments designed for training deep nerd, and cofounder of game
development studio Secret Lab.
reinforcement learning and imitation learning models
Mars Buttfield-Addison is a computing
• Use and apply efficient general-purpose algorithms for and machine learning researcher at the
simulation-based ML, such as proximal policy optimization University of Tasmania.
• Train a variety of ML models using different approaches Tim Nugent is a mobile app developer,
game designer, and computing researcher.
• Enable ML tools to work with industry-standard game
development tools, using PyTorch, and the Unity ML-Agents Jon Manning is a software engineering
expert in Swift, C#, and Objective-C. As
and Perception Toolkits
cofounder of Secret Lab, he created the
popular Yarn Spinner dialog framework
for games.
DATA Twitter: @oreillymedia
linkedin.com/company/oreilly-media
US $59.99 CAN $74.99 youtube.com/oreillymedia
ISBN: 978-1-492-08992-6
, Practical Simulations for
Machine Learning
Using Synthetic Data for AI
Paris and Mars Buttfield-Addison,
Tim Nugent, and Jon Manning
Beijing Boston Farnham Sebastopol Tokyo
