Causal AI

De (autor): Robert Ness

Causal AI

De (autor): Robert Ness

Causal AI is a practical introduction to building AI models that can reason about causality. Robert Ness' 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.

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Editie: Paperback

Nr. pagini: 500

Cod: BRT9781633439917

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Causal AI

De (autor): Robert Ness

Causal AI

De (autor): Robert Ness

Descrierea produsului

S-ar putea sa-ti placa si

The Hundred-Page Machine Learning Book - Andriy Burkov

Neural Networks and Deep Learning: A Textbook - Charu C. Aggarwal

Statistics for Data Science - James D. Miller

Building Intelligent Applications with Generative AI: Explore the potential of AI for next gen applications (English Edition) - Yattish Ramhorry

An Introduction to Causal Inference - Judea Pearl

The Statistical Physics of Data Assimilation and Machine Learning - Henry D. I. Abarbanel

Neural Networks: A Practical Guide For Understanding And Programming Neural Networks And Useful Insights For Inspiring Reinvention - Steven Cooper

Probabilistic Numerics: Computation as Machine Learning - Philipp Hennig

Deep Learning Pocket Reference Guide: A quick pocket reference guide to a collection of deep learning algorithms and key terms - The Data Guy

Elements of Causal Inference: Foundations and Learning Algorithms - Jonas Peters

The Math of Neural Networks - Michael Taylor

Time Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting - Banglore Vijay Kumar Vishwas

Build a Large Language Model (from Scratch) - Sebastian Raschka

Bayesian Optimization: Theory and Practice Using Python - Peng Liu

Artificial Intelligence: An Illustrated History: From Medieval Robots to Neural Networks - Clifford A. Pickover

Inside Deep Learning: Math, Algorithms, Models - Edward Raff

Machine Learning: An Introduction To Supervised & Unsupervised Learning Algorithms - Michael Colins

Variational Bayesian Learning Theory - Shinichi Nakajima

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