Simon Liu – författare

2022 Christianity Today Award of Merit, Missions & Global Church"A powerful and moving collection of sermonic letters" --Tim Challies"If we want revival in our communities, then let us learn from those being revived." For many Western Christians, the experience of suffering and persecution is remote. For Chinese Christians, on the other hand, persecution is a regular aspect of the Christian life. If a Christian from the West is transported to a Chinese house church, the topic of suffering would be ever-present in preaching and conversation. With decades of persecution under government oppression and a rich theology of suffering, the Chinese house church movement has much to contribute theologically to the global church.In Faith in the Wilderness, editors Hannah Nation and Simon Liu pull together the insights of the Chinese church for the West. These sermonic letters form Chinese Christians pull back the curtain on the pastoral heart and the hope behind the house church's remarkable faithfulness, awakening readers to the reality of the gospel--the ground of our hope--in the midst of darkness. Readers will be convicted, encouraged, and edified by the testimony of these Chinese Christians."Let us learn from the witness of our Chinese brothers and sisters so that we can stand fast all the better as we face trials wherever we live." --Timothy Keller, from the foreword

This book presents a practical, end-to-end approach to risk management in modern e-commerce platforms using machine learning. It covers buyer abuse, seller governance, collusion networks, and payment and credit risk, showing how behavioural, transactional, and network data are translated into actionable risk signals. The book discusses a range of modelling techniques—including tree-based ensembles, sequence and transformer models, graph neural networks, and anomaly detection—focusing on their application under real-world constraints such as class imbalance, latency, adversarial drift, fairness, and regulatory requirements. Extending beyond model development, it addresses deployment, monitoring, governance, and operational assurance, making it relevant to industry practitioners and suitable for advanced courses in applied, industry-focused machine learning.

This book presents a practical, end-to-end approach to risk management in modern e-commerce platforms using machine learning. It covers buyer abuse, seller governance, collusion networks, and payment and credit risk, showing how behavioural, transactional, and network data are translated into actionable risk signals. The book discusses a range of modelling techniques—including tree-based ensembles, sequence and transformer models, graph neural networks, and anomaly detection—focusing on their application under real-world constraints such as class imbalance, latency, adversarial drift, fairness, and regulatory requirements. Extending beyond model development, it addresses deployment, monitoring, governance, and operational assurance, making it relevant to industry practitioners and suitable for advanced courses in applied, industry-focused machine learning.

This textbook is a consolidation of learning methods which comes in an analytic form. The covered learning methods include classical and advanced solutions to problems of regression, minimum classification error, maximum receiver operating characteristics, bridge regression, ensemble learning and network learning. Both the primal and dual solution forms are discussed for over-and under-determined systems. Such coverage provides an important perspective for handling systems with overwhelming samples or systems with overwhelming parameters. For goal driven classification, the solutions to minimum classification-error, maximum receiver operating characteristics, bridge regression, and ensemble learning represent recent advancements in the literature. In this book, the exercises offer instructors and students practical experience with real-world applications.