Name: Practical Simulations for Machine Learning: Using Synthetic Data for ai (en Inglés)
Price: 71.40 USD
Availability: InStock
Author: Buttfield-Addison, Paris ; Buttfield-Addison, Mars ; Nugent, Tim
ISBN: 9781492089926

portada Practical Simulations for Machine Learning: Using Synthetic Data for ai (en Inglés)

Practical Simulations for Machine Learning: Using Synthetic Data for ai (en Inglés)

Formato

Libro Físico

Autor

Tim Nugent

Paris Buttfield-Addison

Mars Buttfield-Addison

Editorial

O'reilly Media

Año

2022

Idioma

Inglés

N° páginas

331

Encuadernación

Tapa Blanda

Dimensiones

23.3 x 17.8 x 2.3 cm

Peso

0.54 kg.

ISBN13

9781492089926

N° edición

Categorías

Aprendizaje automático («machine learning»)

Practical Simulations for Machine Learning: Using Synthetic Data for ai (en Inglés)

Tim Nugent (Autor) · Paris Buttfield-Addison (Autor) · Mars Buttfield-Addison (Autor) · O'reilly Media · Tapa Blanda

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Reseña del libro "Practical Simulations for Machine Learning: Using Synthetic Data for ai (en Inglés)"

Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models. Thatâ s just the beginning. With this practical book, youâ ll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits