THE ULTIMATE GIFT FOR WANNABE SPIES: AN INTERACTIVE PUZZLE BOOK FROM THE CREATOR OF THE BESTSELLING CULT PHENOMENON, JOURNAL 29! In this brain-bending interactive game, you'll solve puzzles on every page, and obtain keys to move forward by submitting answers online. To solve each puzzle, you'll need to think outside the book. You are an agent of CY.P.H.E.R., the secret international agency working on 'unsolvable' code-based cases. Called upon to investigate cryptic clues discovered in the wake of a series of mysterious disappearances, the clock is ticking to crack the codes before it's too late. To escape this book, you must write, draw, search, fold and cut pages, explore virtual escape rooms and think laterally to identify the perpetrator and solve the mystery. All you need to play is a pencil, a pair of scissors, an internet connection, and a curious mind.

Journal 29 is a unique book game where you can solve riddles and puzzles and submit your answers online to get the keys and move forward.To solve the riddles, you need to think out of the box.You can write, draw, search, fold pages, combine different methods and try to get those riddles right.Journal 29 is a 148 pages book providing over 63 riddles you can solve.

THE ULTIMATE GIFT FOR WANNABE SPIES: AN INTERACTIVE PUZZLE BOOK FROM THE CREATOR OF THE BESTSELLING CULT PHENOMENON, JOURNAL 29! In this brain-bending interactive game, you'll solve puzzles on every page, and obtain keys to move forward by submitting answers online. To solve each puzzle, you'll need to think outside the book. You are an agent of CY.P.H.E.R., the secret international agency working on 'unsolvable' code-based cases. Called upon to investigate cryptic clues discovered in the wake of a series of mysterious disappearances, the clock is ticking to crack the codes before it's too late. To escape this book, you must write, draw, search, fold and cut pages, explore virtual escape rooms and think laterally to identify the perpetrator and solve the mystery. All you need to play is a pencil, a pair of scissors, an internet connection, and a curious mind.

Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook Description Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data. What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is for This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.

This book presents original contributions on the theories and practices of emerging Internet, data and Web technologies and their applicability in businesses, engineering and academia, focusing on advances in the life-cycle exploitation of data generated from the digital ecosystem data technologies that create value, e.g. for businesses, toward a collective intelligence approach. The Internet has become the most proliferative platform for emerging large-scale computing paradigms. Among these, data and web technologies are two of the most prominent paradigms and are found in a variety of forms, such as data centers, cloud computing, mobile cloud, and mobile Web services. These technologies together create a digital ecosystem whose cornerstone is the data cycle, from capturing to processing, analyzing and visualizing. The investigation of various research and development issues in this digital ecosystem are made more pressing by the ever-increasing requirements of real-world applications that are based on storing and processing large amounts of data. The book is a valuable resource for researchers, software developers, practitioners and students interested in the field of data and web technologies.

When greed takes over man, there is no stopping him. Even the nation’s interests are of little concern to him! Enemies Within is a thrilling crime novel that sharply reveals the consequences of human fallacy. When Squadron Leader, Sudhir, suspects Flight Lieutenant Sunder of nefarious activities, he knows he has to act fast before things get out of hand. With the help of his wife, Poonam, Sudhir attempts to crack down a dangerous spy network, which is secretly passing on sensitive information about India to her enemies. Poonam is a big revelation, as she boldly risks her life to thwart the activities of the espionage gang and save her nation. Replete with twists and turns, suspense, emotions and high drama, Enemies Within will keep you at the edge of your seat, till the very end.

Practical methods for analyzing your data with graphs, revealing hidden connections and new insights. Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. Foreword by Michael Hunger. About the technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's inside Creating knowledge graphs Node classification and link prediction workflows NLP techniques for graph construction About the reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the author Tomaž Bratanic works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Table of Contents PART 1 INTRODUCTION TO GRAPHS 1 Graphs and network science: An introduction 2 Representing network structure: Designing your first graph model PART 2 SOCIAL NETWORK ANALYSIS 3 Your first steps with Cypher query language 4 Exploratory graph analysis 5 Introduction to social network analysis 6 Projecting monopartite networks 7 Inferring co-occurrence networks based on bipartite networks 8 Constructing a nearest neighbor similarity network PART 3 GRAPH MACHINE LEARNING 9 Node embeddings and classification 10 Link prediction 11 Knowledge graph completion 12 Constructing a graph using natural language processing technique