This book includes 18 chapters in six parts that summarize various research results and associated development activities on the Language Grid. The chapters in Part I describe the framework of the Language Grid, i.e., service-oriented collective intelligence, used to bridge providers, users and operators. Two kinds of software are introduced, the service grid server software and the Language Grid Toolbox, and code for both is available via open source licenses. Part II describes technologies for service workflows that compose atomic language services. Part III reports on research work and activities relating to sharing and using language services. Part IV describes various applications of language services as applicable to intercultural collaboration. Part V contains reports on applying the Language Grid for translation activities, including localization of industrial documents and Wikipedia articles. Finally, Part VI illustrates how the Language Grid can be connected to other service grids, such as DFKI's Heart of Gold and smart classroom services in Tsinghua University in Beijing.
The book will be valuable for researchers in artificial intelligence, natural language processing, services computing and human--computer interaction, particularly those who are interested in bridging technologies and user communities.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
The authors are specialists in diverse areas such as informatics, engineering, agriculture, sociology and pedagogy, and their areas of interest range from environment conservation to social education for international cooperation. They have a particular focus on the environment in southeast Asia and related topics such as large-scale traffic simulations, participatory workshops, inclusive design workshops, distance learning, and intercultural collaboration.This book targets graduate students seeking tools and methodologies for natural observation, field workers engaged in social participation, and researchers and engineers pursuing innovation. The techniques described in the book could also be exploited by government officials to form consensus and develop activities or by non-profit organizations to undertake more effective social programs.
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data
The research based on the authors’ operating experiences of handling complicated issues such as intellectual property and interoperability of language resources contributes to exploitation of language resources as a service. On the other hand, both the analysis based on using services and the design of new services can bring significant results. A new style of multilingual communication supported by language services is worthy of analysis in HCI/CSCW, and the design process of language services is the focus of valuable case studies in service science. By using language resources in different ways based on the Language Grid, many activities are highly regarded by diverse communities.
This book consists of four parts: (1) two types of language service platforms to interconnect language services across service grids, (2) various language service composition technologies that improve the reusability, efficiency, and accuracy of composite services, (3) research work and activities in creating language resources and services, and (4) various applications and tools for understanding and designing language services that well support intercultural collaboration.