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ASP.NET Core 6 and Angular 5th by Valerio De Sanctis × 2 |
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The Raveling: The Complete Saga × 1 |
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Ottolenghi Simple_ A Cookbook × 1 |
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Greek Lyric: An Anthology in Translation (Hackett Classics) × 1 |
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Code Talker: The First and Only Memoir By One of the Original Navajo Code Talkers of WWII × 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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Feed the Planet_ A Photographic Journey to the World's Food × 1 |
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Held by the Land: A Guide to Indigenous Plants for Wellness × 1 |
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Anees Hussain MCAT Set 2024 × 1 |
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Quick and Easy Italian Recipes Regular priceRs.3,600.00 PKR × 1 |
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ATI TEAS Science Questions_ Questions & Explanations for Test of Essential Academic Skills (TEAS) ATI TEAS Science Questions_ Questions & Explanations for Test of Essential Academic Skills (TEAS) × 1 |
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The Art of the Sonnet × 1 |
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Everyday Korean : Fresh, Modern Recipes for Home Cooks (Hardcover) × 1 |
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100 Summaries and Reviews of Timeless World Literature Classics: Masterpieces you need to know × 1 |
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Joyberg Aesthetic Spiral Notebook, 2PCS Cute Notebook, 8.3_ x 6_ Notebook For Women, A5 Spiral Journal Notebook for × 1 |
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Artificial Intelligence A Textbook by Charu Aggarwal × 1 |
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Elegy × 1 |
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Algorithm Design and Applications by Michael T Goodrich × 1 |
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Deadly Pick-Up, The & Killer Take All! × 1 |
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Oxford Spiral Notebook, 1 Subject, College Ruled Paper, 8 x 10-1_2 Inch, Pastel Pink, Orange, Yellow, Green, Blue and Purple, × 1 |
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Olive Oil Baking_ Heart-Healthy Recipes That Increase Good Cholesterol and Reduce Saturated Fats × 1 |
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