Deep Search collects 13 texts which investigate the social and political dimensions of how we navigate the deep seas of knowledge. What do we win, and what do we lose when we move from an analogue to a digital information order? How is computer readable significance produced, how is meaning involved in machine communication? Where is the potential of having access to such vast amounts of information? What are the dangers of our reliance on search engines and are there any approaches that do not follow the currently dominating paradigm of Google? This volume answers these questions of culture, context and classification regarding information systems that should not be ignored.

Summary Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on! Foreword by Chris Mattmann. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning handles the toughest search challenges, including imprecise search terms, badly indexed data, and retrieving images with minimal metadata. And with modern tools like DL4J and TensorFlow, you can apply powerful DL techniques without a deep background in data science or natural language processing (NLP). This book will show you how. About the Book Deep Learning for Search teaches you to improve your search results with neural networks. You'll review how DL relates to search basics like indexing and ranking. Then, you'll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you'll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn! What's inside Accurate and relevant rankings Searching across languages Content-based image search Search with recommendations About the Reader For developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed. About the Author Tommaso Teofili is a software engineer with a passion for open source and machine learning. As a member of the Apache Software Foundation, he contributes to a number of open source projects, ranging from topics like information retrieval (such as Lucene and Solr) to natural language processing and machine translation (including OpenNLP, Joshua, and UIMA). He currently works at Adobe, developing search and indexing infrastructure components, and researching the areas of natural language processing, information retrieval, and deep learning. He has presented search and machine learning talks at conferences including BerlinBuzzwords, International Conference on Computational Science, ApacheCon, EclipseCon, and others. You can find him on Twitter at @tteofili. Table of Contents PART 1 - SEARCH MEETS DEEP LEARNING Neural search Generating synonyms PART 2 - THROWING NEURAL NETS AT A SEARCH ENGINE From plain retrieval to text generation More-sensitive query suggestions Ranking search results with word embeddings Document embeddings for rankings and recommendations PART 3 - ONE STEP BEYOND Searching across languages Content-based image search A peek at performance

Follow pastor Jim Belcher and his family as they take a pilgrimage through Europe, seeking substance for their faith in Christianity's historic, civilizational home. What they find, in places like Lewis's Oxford and Bonhoeffer's Germany, are glimpses of another kind of faith—one with power to cut through centuries and pierce our hearts today.

"The best book on high school dynamics I have ever read."--Jay Mathews, Washington Post An award-winning professor and an accomplished educator take us beyond the hype of reform and inside some of America's most innovative classrooms to show what is working--and what isn't--in our schools. What would it take to transform industrial-era schools into modern organizations capable of supporting deep learning for all? Jal Mehta and Sarah Fine's quest to answer this question took them inside some of America's most innovative schools and classrooms--places where educators are rethinking both what and how students should learn. The story they tell is alternately discouraging and hopeful. Drawing on hundreds of hours of observations and interviews at thirty different schools, Mehta and Fine reveal that deeper learning is more often the exception than the rule. And yet they find pockets of powerful learning at almost every school, often in electives and extracurriculars as well as in a few mold-breaking academic courses. These spaces achieve depth, the authors argue, because they emphasize purpose and choice, cultivate community, and draw on powerful traditions of apprenticeship. These outliers suggest that it is difficult but possible for schools and classrooms to achieve the integrations that support deep learning: rigor with joy, precision with play, mastery with identity and creativity. This boldly humanistic book offers a rich account of what education can be. The first panoramic study of American public high schools since the 1980s, In Search of Deeper Learning lays out a new vision for American education--one that will set the agenda for schools of the future.

This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.

What Is Depth First Search An technique known as depth-first search, or DFS, is used to search or traverse data structures that are organized as trees or graphs. The algorithm makes its way outward from the root node in order to travel as far as it can along each branch before retracing its steps. To aid in the process of backtracking through the graph, additional memory, typically in the form of a stack, is required in order to keep track of the nodes that have been discovered so far along a particular branch. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Depth-First Search Chapter 2: Graphs in Discrete Mathematics Chapter 3: Recursion in Computer Science Chapter 4: Stack Abstract Data Type Chapter 5: Topological Sorting Chapter 6: Algorithmic Efficiency Chapter 7: Randomized Algorithm Chapter 8: Bidirectional Search Chapter 9: Parallel Computing Chapter 10: Analysis of Algorithms (II) Answering the public top questions about depth first search. (III) Real world examples for the usage of depth first search in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of depth first search' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of depth first search.