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(ISC)2 Cissp Certified Information Systems Security Professional Official Practice Tests by Mike Chapple
Mike Chapple
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(ISC)2 Cissp Certified Information Systems Security Professional Official Practice Tests by Mike Chapple
Mike Chapple
₨3,300.00
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(ISC)2 CISSP Certified Information Systems Security Professional Official Study Guide 9th by Mike Chapple
Mike Chapple
₨5,300.00

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(ISC)2 CISSP Certified Information Systems Security Professional Official Study Guide 9th by Mike Chapple
Mike Chapple
₨5,300.00
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1000 Python Examples by Gábor Szabó
Gábor Szabó
₨4,300.00

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1000 Python Examples by Gábor Szabó
Gábor Szabó
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6G: The Next Horizon: From Connected People and Things to Connected Intelligence 1st by Wen Tong
Wen Tong
₨2,300.00

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6G: The Next Horizon: From Connected People and Things to Connected Intelligence 1st by Wen Tong
Wen Tong
₨2,300.00
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Advanced Machine Learning with Python by John Hearty
John Hearty
₨1,400.00

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Advanced Machine Learning with Python by John Hearty
John Hearty
₨1,400.00
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Advanced R 2nd By Hadley Wickham
Hadley Wickham
₨2,650.00

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Advanced R 2nd By Hadley Wickham
Hadley Wickham
₨2,650.00
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Advanced R 4 Data Programming and the Cloud 2nd by Matt Wiley
Matt Wiley
₨2,050.00

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Advanced R 4 Data Programming and the Cloud 2nd by Matt Wiley
Matt Wiley
₨2,050.00
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Advanced R Statistical Programming and Data Models by Matt Wiley
Matt Wiley
₨2,800.00

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Advanced R Statistical Programming and Data Models by Matt Wiley
Matt Wiley
₨2,800.00
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Advanced Unity Game Development 1st by Victor G Brusca
Victor G Brusca
₨1,750.00

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Advanced Unity Game Development 1st by Victor G Brusca
Victor G Brusca
₨1,750.00
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An Atypical ASP.NET Core 6 Design Patterns Guide 2nd by Carl-Hugo Marcotte
Carl-Hugo Marcotte
₨3,000.00

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An Atypical ASP.NET Core 6 Design Patterns Guide 2nd by Carl-Hugo Marcotte
Carl-Hugo Marcotte
₨3,000.00
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Android Programming 4th by Bill Phillips
Bill Phillips
₨2,750.00

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Android Programming 4th by Bill Phillips
Bill Phillips
₨2,750.00
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Android Programming for Beginners 3rd by John Horton
John Horton
₨3,270.00

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Android Programming for Beginners 3rd by John Horton
John Horton
₨3,270.00
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Androids by Chet Haase
₨2,100.00

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Androids by Chet Haase
₨2,100.00
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Angular: From Theory To Practice by by Asim Hussain
Asim Hussain
₨3,700.00

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Angular: From Theory To Practice by by Asim Hussain
Asim Hussain
₨3,700.00
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Architecture Patterns with Python by Harry J.W. Percival
Harry J.W. Percival
₨2,200.00

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Architecture Patterns with Python by Harry J.W. Percival
Harry J.W. Percival
₨2,200.00
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Artificial Intelligence and Causal Inference 1st By Momiao Xiong
Momiao Xiong
₨1,700.00

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Artificial Intelligence and Causal Inference 1st By Momiao Xiong
Momiao Xiong
₨1,700.00
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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.
₨1,100.00

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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.
₨1,100.00
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ASP.NET Core 6 and Angular 5th by Valerio De Sanctis
Valerio De Sanctis
₨3,400.00

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ASP.NET Core 6 and Angular 5th by Valerio De Sanctis
Valerio De Sanctis
₨3,400.00
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Atomic Kotlin by Bruce Eckel
Bruce Eckel
₨2,300.00

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Atomic Kotlin by Bruce Eckel
Bruce Eckel
₨2,300.00
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Automate the Boring Stuff with Python: Practical Programming forTotal Beginners by Al Sweigart
Al Sweigart
₨2,350.00

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Automate the Boring Stuff with Python: Practical Programming forTotal Beginners by Al Sweigart
Al Sweigart
₨2,350.00
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