Verificar estado da encomenda

Faça parte de uma comunidade de amantes de livros de todo o mundo e tenha acesso a uma série de benefícios. Crie uma conta gratuitamente

Correio DHL 8.49 € Correio DPD 4.99 € Ponto DPD 4.49 € Correio GLS 5.99 € Correio MRW 5.99 € Ponto GLS 4.99 €

Contacto

Como comprar

Ajuda

A minha conta

▸ Vazio :-(

Causal AI

Name: Causal AI
Brand: Manning Publications
SKU: 46056807
Price: 73.68 EUR
Availability: InStock
Author: Ness
ISBN: 9781633439917

Ness

Robert Osazuwa

Língua

Inglês

Livro Capa mole

Código Libristo: 46056807

Editoras Manning Publications, setembro 2024

How do you know what might have happened, had you done things differently? Causal machine learning... Descrição completa

Código Libristo: 46056807

179 b

73.68 €

50% de hipóteses Procuremos no mundo Quando vou receber o meu livro?

Até 30 dias para devoluções

Os clientes também compraram

Causal Inference and Discovery in Python Aleksander Molak

Capa mole

53.57 €

Asian Financial Crisis 1995-98 Russell Napier

Livro de capa dura

50.16 €

The Price of Time: The Real Story of Interest CHANCELLOR EDWARD

Capa mole

16.08 €

History of Interest Rates 4e Sidney Homer

Livro de capa dura

79.91 €

How do you know what might have happened, had you done things differently? Causal machine learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions.

In Causal AI you will learn how to:

Build causal reinforcement learning algorithms
Implement causal inference with modern probabilistic machine tools such as PyTorch and Pyro
Compare and contrast statistical and econometric methods for causal inference
Set up algorithms for attribution, credit assignment, and explanation
Convert domain expertise into explainable causal models

Causal AI is a practical introduction to building AI models that can reason about causality. Author Robert Ness, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes.

About the technology:

Causal machine learning is a major milestone in machine learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars.