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Web Hosting Blues_ It's Got to Be a Better Way (Simplify It) × 1 |
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As a Man Thinketh - The Original 1902 Classic (includes The Mastery of Destiny) (Reader's Library Classics) × 1 |
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An Illustrated Treasury of Hans Christian Andersen's Fairy Tales: The Little Mermaid, Thumbelina, The Princess and the Pea and many more classic stories × 1 |
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The Watcher (Bigler County Romantic Thrillers Book 1) × 1 |
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Dark Psychology for Beginners : 15 Simple Techniques Will Help Protect You from Negative Manipulation. You Learn to Use the Secrets of Body Language, Persuasion, and Human Behavior. × 1 |
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Advanced Unity Game Development 1st by Victor G Brusca × 1 |
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Hooked: A Dark, Contemporary Romance (Never After Series) × 1 |
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First Person × 1 |
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Psychological and Philosophical Implications of Anomalous Experience_ Information and Applications for Clinical Practice × 1 |
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The Ballad of Buster Scruggs × 1 |
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Kitchen Gardening for Beginners: A Simple Guide to Growing Fruit and Vegetables × 1 |
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Elegy for Kosovo × 1 |
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Introduction to Soil Science × 1 |
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Biochemistry Behind the Symptoms (2) × 1 |
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Toast: The Cookbook × 1 |
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Carbs & Cals World Foods_ A visual guide to African, Arabic, Caribbean and South Asian foods for diabetes & weight management × 1 |
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Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. Key Features: Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training. Deep learning for nonlinear mediation and instrumental variable causal analysis. Construction of causal networks is formulated as a continuous optimization problem. Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks. Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes. AI-based methods for estimation of individualized treatment effect in the presence of network interference. Table of Contents Chapter 1 Deep Neural Networks Chapter 2 Gaussian Processes and Learning Dynamic for Wide Neural Networks Chapter 3 Deep Generative Models Chapter 4 Generative Adversarial Networks Chapter 5 Deep Learning For Causal Inference Chapter 6 Causal Inference in Time Series Chapter 7 Deep Learning for Counterfactual Inference and Treatment Effect Estimation Chapter 8 Reinforcement Learning and Causal Author(s) Biography Momiao Xiong, is a professor in the Department of Biostatistics and Data Science, University of Texas School of Public Health, and a regular member in the Genetics & Epigenetics (G&E) Graduate Program at The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Science. His interests are artificial intelligence, causal inference, bioinformatics and genomics. × 1 |
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A Man Called Justice: A Classic Western Series with Heart (Silent Justice Book 1) × 1 |
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