Di Wu – författare

Against the background of China''s rapidly growing, and sometimes highly controversial, activities in Africa, this book is among the first of its kind to systematically document Sino-African interactions at the everyday level.

Based on sixteen months of ethnographic fieldwork at two contrasting sites in Lusaka, Zambia—a Chinese state-sponsored educational farm and a private Chinese family farm—Di Wu focuses on daily interactions among Chinese migrants and their Zambian hosts. Daily communicative events, e.g. banquets, market negotiations, work-place disputes, and various social encounters across a range of settings are used to trace the essential role that emotion/affect plays in forming and reproducing social relations and group identities among Chinese migrants. Wu suggests that affective encounters in everyday situations—as well as failed attempts to generate affect—should not be overlooked in order to fully appreciate Sino-African interactions.

Deeply researched and with rich ethnographic detail, this book will be relevant to scholars of anthropology, international development, and others interested in Sino-African relations.

Against the background of China''s rapidly growing, and sometimes highly controversial, activities in Africa, this book is among the first of its kind to systematically document Sino-African interactions at the everyday level.

Based on sixteen months of ethnographic fieldwork at two contrasting sites in Lusaka, Zambia—a Chinese state-sponsored educational farm and a private Chinese family farm—Di Wu focuses on daily interactions among Chinese migrants and their Zambian hosts. Daily communicative events, e.g. banquets, market negotiations, work-place disputes, and various social encounters across a range of settings are used to trace the essential role that emotion/affect plays in forming and reproducing social relations and group identities among Chinese migrants. Wu suggests that affective encounters in everyday situations—as well as failed attempts to generate affect—should not be overlooked in order to fully appreciate Sino-African interactions.

Deeply researched and with rich ethnographic detail, this book will be relevant to scholars of anthropology, international development, and others interested in Sino-African relations.

Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and Data Mining with Python focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge.

The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study.

This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work.

Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and Data Mining with Python focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge.

The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study.

This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work.

As an introduction to Python, this book allows readers to take a slow and steady approach to understanding Python code, explaining concepts, connecting programming with real-life examples, writing Python programs, and completing case studies.

While there are many books, websites, and online courses about the topic, we break down Python programming into easily digestible lessons of less than 5 minutes each, following our BiteSize approach. Each lesson begins with a clear and short introduction to the topic. This gives you a strong base to start from and gets you ready for deeper learning. Then, you will see coding demonstrations that show the ideas discussed. These examples are simple and useful, helping you really understand the concepts. You’ll then practice tasks at different difficulty levels, so you can test your knowledge and increase your confidence. You’ll also play with case studies to solve real-world problems. Tips are included to show how you can incorporate generative AI into your learning toolkit, using it for feedback, practice exercises, code reviews, and exploring advanced topics. Recommended AI prompts can help you identify areas for improvement, review key concepts, and track your progress.

This book is designed for absolute beginners with no prior programming experience. It is ideal for individuals with busy schedules or limited time for studying.

As an introduction to Python, this book allows readers to take a slow and steady approach to understanding Python code, explaining concepts, connecting programming with real-life examples, writing Python programs, and completing case studies.

While there are many books, websites, and online courses about the topic, we break down Python programming into easily digestible lessons of less than 5 minutes each, following our BiteSize approach. Each lesson begins with a clear and short introduction to the topic. This gives you a strong base to start from and gets you ready for deeper learning. Then, you will see coding demonstrations that show the ideas discussed. These examples are simple and useful, helping you really understand the concepts. You’ll then practice tasks at different difficulty levels, so you can test your knowledge and increase your confidence. You’ll also play with case studies to solve real-world problems. Tips are included to show how you can incorporate generative AI into your learning toolkit, using it for feedback, practice exercises, code reviews, and exploring advanced topics. Recommended AI prompts can help you identify areas for improvement, review key concepts, and track your progress.

This book is designed for absolute beginners with no prior programming experience. It is ideal for individuals with busy schedules or limited time for studying.

Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty.

In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learningusing L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.