Bo Wang

Feng Wang

Shubham Saboo

Cristian Mitroi

Susana Guzman

Implement neural search systems on the cloud by leveraging Jina design patterns Key FeaturesIdentify the different search techniques and discover applications of neural searchGain a solid understanding of vector representation and apply your knowledge in neural searchUnlock deeper levels of knowledge of Jina for neural searchBook Description Search is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search. Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the time and complexity involved in creating deep learning–powered search engines. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search engine. With the help of step-by-step explanations, practical examples, and self-assessment questions, you'll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search engine. By the end of this deep learning book, you'll be able to make the most of Jina's neural search design patterns to build an end-to-end search solution for any modality. What you will learnUnderstand how neural search and legacy search workGrasp the machine learning and math fundamentals needed for neural searchGet to grips with the foundation of vector representationExplore the basic components of JinaAnalyze search systems with different modalitiesUncover the capabilities of Jina with the help of practical examplesWho this book is for If you are a machine learning, deep learning, or artificial intelligence engineer interested in building a search system of any kind (text, QA, image, audio, PDF, 3D models, or others) using modern software architecture, this book is for you. This book is perfect for Python engineers who are interested in building a search system of any kind using state-of-the-art deep learning techniques.

Build deep learning–powered search systems that you can deploy and manage with ease

Neural Search - From Prototype to Production with Jina

Haiyan Wang

Kuai Xu

The book lies at the interface of mathematics, social media analysis, and data science. Its authors aim to introduce a new dynamic modeling approach to the use of partial differential equations for describing information diffusion over online social networks. The eigenvalues and eigenvectors of the Laplacian matrix for the underlying social network are used to find communities (clusters) of online users. Once these clusters are embedded in a Euclidean space, the mathematical models, which are reaction-diffusion equations, are developed based on intuitive social distances between clusters within the Euclidean space. The models are validated with data from major social media such as Twitter. In addition, mathematical analysis of these models is applied, revealing insights into information flow on social media. Two applications with geocoded Twitter data are included in the book: one describing the social movement in Twitter during the Egyptian revolution in 2011 and another predicting influenza prevalence. The new approach advocates a paradigm shift for modeling information diffusion in online social networks and lays the theoretical groundwork for many spatio-temporal modeling problems in the big-data era.

Modeling Information Diffusion in Online Social Networks with Partial Differential Equations

Ting Hu

Qian Wang

Hongwei Li

This book constitutes the workshop proceedings of the 18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2018, held in Guangzhou, China, in November 2018. The 24 full papers presented were carefully selected and reviewed from numerous submissions to the two following workshops: - ICA3PP 2018 Workshop on Intelligent Algorithms for Large-scale Complex Optimization Problems - ICA3PP 2018 Workshop on Security and Privacy in Data Processing

ICA3PP 2018 International Workshops, Guangzhou, China, November 15-17, 2018, Proceedings

Algorithms and Architectures for Parallel Processing

Xiao Hui Wang

1986 - 1997 ; Shanghai, Beijing, Tianjin

Wang Xiao Hui Photographic Review

Deborah Davis

Presents an up-to-date look at the social processes and consequences of China's rapid economic growth.

Creating Wealth and Poverty in Postsocialist China

Feng Wang

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