Big data
Grafowe sieci neuronowe. Teoria i praktyka
Filip Wójcik
Cicha rewolucja, która nadeszła Grafowe sieci neuronowe (ang. graph neural networks, GNN) to klasa modeli uczenia głębokiego przeznaczona do analizy danych o strukturze grafowej. W początkowym okresie ich rozwój ograniczał brak efektywnych metod projektowania i optymalizacji; w ostatnich latach bariery te w dużej mierze zostały pokonane, co przełożyło się na dynamiczny postęp teorii i praktyki. Modele GNN znajdują zastosowanie między innymi w analizie sieci społecznościowych, optymalizacji procesów logistycznych, marketingu i pracy z bazami wiedzy. Ta książka zawiera kompleksowe opracowanie tematyki sieci grafowych w kontekście uczenia maszynowego. Tym samym wypełnia istotną lukę na polskim rynku wydawniczym, oferując połączenie solidnych podstaw teoretycznych z praktycznym zastosowaniem GNN. To przewodnik, który systematycznie przeprowadza przez kolejne zagadnienia związane z sieciami grafowymi: od narzędzi klasycznej analizy grafów w środowisku Pythona i wybranych zagadnień teorii grafów przez wprowadzenie do grafowych sieci neuronowych, a także przegląd wybranych warstw splotu grafowego i dobrych praktyk ich projektowania po zagadnienia związane ze szkoleniem sieci GNN i praktyczne przykłady ich zastosowań
Gary Hutson, Matt Jackson
Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis.Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements.By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.
Ravindranatha Anthapu
While it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way.This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You’ll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users.After having worked through this book, you’ll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.
Aldo Marzullo, Enrico Deusebio, Claudio Stamile
Graph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools.By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.*Email sign-up and proof of purchase required -
Claudio Stamile, Aldo Marzullo, Enrico Deusebio
Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You’ll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you’ll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You’ll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.
Guerrilla Data Analysis Using Microsoft Excel. Overcoming Crap Data and Excel Skirmishes
MrExcel's Holy Macro! Books, Oz du Soleil,...
Unlock Microsoft Excel's hidden potential with this dynamic guide designed for data professionals and enthusiasts. You'll start by reviewing Excel basics before advancing to powerful tools like Excel Tables, Pivot Tables, and Power Query. Each chapter enhances your ability to analyze and visualize data efficiently, from complex lookups and dynamic arrays to essential data validation techniques that ensure accuracy and integrity in your spreadsheets.As you progress, you'll learn how to protect your work with advanced sheet protection methods and collaboration tools for seamless teamwork. The book also covers sophisticated functions like INDIRECT, OFFSET, and LET, preparing you to tackle complex data challenges. Additionally, you'll receive critical advice on avoiding the pitfalls of machine learning-driven features and maintaining clean, organized data.By the end of the guide, you'll have mastered Excel's advanced capabilities, empowering you to streamline workflows, optimize data processes, and make confident, data-driven decisions. This guide is your comprehensive resource for transforming your approach to data analysis with Excel.
Hadoop 2.x Administration Cookbook. Administer and maintain large Apache Hadoop clusters
Aman Singh
Hadoop enables the distributed storage and processing of large datasets across clusters of computers. Learning how to administer Hadoop is crucial to exploit its unique features. With this book, you will be able to overcome common problems encountered in Hadoop administration.The book begins with laying the foundation by showing you the steps needed to set up a Hadoop cluster and its various nodes. You will get a better understanding of how to maintain Hadoop cluster, especially on the HDFS layer and using YARN and MapReduce. Further on, you will explore durability and high availability of a Hadoop cluster. You’ll get a better understanding of the schedulers in Hadoop and how to configure and use them for your tasks. You will also get hands-on experience with the backup and recovery options and the performance tuning aspects of Hadoop. Finally, you will get a better understanding of troubleshooting, diagnostics, and best practices in Hadoop administration.By the end of this book, you will have a proper understanding of working with Hadoop clusters and will also be able to secure, encrypt it, and configure auditing for your Hadoop clusters.
Sudheesh Narayan, Tanmay Deshpande, Anurag Shrivastava
If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level.Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book.The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space.
Hadoop: Data Processing and Modelling. Data Processing and Modelling
Sandeep Karanth, Gerald Turkington, Tanmay Deshpande
As Marc Andreessen has said “Data is eating the world,” which can be witnessed today being the age of Big Data, businesses are producing data in huge volumes every day and this rise in tide of data need to be organized and analyzed in a more secured way. With proper and effective use of Hadoop, you can build new-improved models, and based on that you will be able to make the right decisions.The first module, Hadoop beginners Guide will walk you through on understanding Hadoop with very detailed instructions and how to go about using it. Commands are explained using sections called “What just happened” for more clarity and understanding. The second module, Hadoop Real World Solutions Cookbook, 2nd edition, is an essential tutorial to effectively implement a big data warehouse in your business, where you get detailed practices on the latest technologies such as YARN and Spark.Big data has become a key basis of competition and the new waves of productivity growth. Hence, once you get familiar with the basics and implement the end-to-end big data use cases, you will start exploring the third module, Mastering Hadoop. So, now the question is if you need to broaden your Hadoop skill set to the next level after you nail the basics and the advance concepts, then this course is indispensable. When you finish this course, you will be able to tackle the real-world scenarios and become a big data expert using the tools and the knowledge based on the various step-by-step tutorials and recipes.
Hadoop. Komplety przewodnik. Analiza i przechowywanie danych
Tom White
Analiza danych z Hadoopem — i wszystko staje się prostsze! Podstawy Hadoopa i model MapReduce Praca z Hadoopem, budowa klastra i zarządzanie platformą Dodatki zwiększające funkcjonalność Hadoopa Platforma Apache Hadoop to jedno z zaawansowanych narzędzi informatycznych. Dzięki niej można przeprowadzać różne operacje na dużych ilościach danych i znacznie skrócić czas wykonywania tych działań. Wszędzie tam, gdzie potrzebne jest szybkie sortowanie, obliczanie i archiwizowanie danych — np. w dużych międzynarodowych sklepach internetowych, serwisach społecznościowych lub wyszukiwarkach, takich jak Amazon, Facebook, Yahoo!, Apache Hadoop sprawdza się znakomicie. Jeśli potrzebne Ci narzędzie do poważnej analizy dużych zbiorów danych, nie znajdziesz lepszego rozwiązania! Tę książkę napisał wytrawny znawca i współtwórca Hadoopa. Przedstawia w niej wszystkie istotne mechanizmy działania platformy i pokazuje, jak efektywnie jej używać. Dowiesz się stąd, do czego służą model MapReduce oraz systemy HDFS i YARN. Nauczysz się budować aplikacje oraz klastry. Poznasz dwa formaty danych, a także wykorzystasz narzędzia do ich pobierania i transferu. Sprawdzisz, jak wysokopoziomowe narzędzia do przetwarzania danych współdziałają z Hadoopem. Zorientujesz się, jak działa rozproszona baza danych i jak zarządzać konfiguracją w środowisku rozproszonym. Przeczytasz również o nowinkach w Hadoopie 2 i prześledzisz studia przypadków ilustrujące rolę Hadoopa w systemach służby zdrowia i przy przetwarzaniu danych o genomie. Hadoop i model MapReduce Systemy HDFS i YARN Operacje wejścia – wyjścia w platformie Hadoop Typy, formaty, funkcje i budowa aplikacji w modelu MapReduce Zarządzanie platformą Hadoop Avro, Parquet, Flume i Sqoop — metody pracy z danymi Pig, Hive, Crunch i Spark — wysokopoziomowe narzędzia do przetwarzania danych HBase i ZooKeeper — praca w środowisku rozproszonym Integrowanie danych w firmie Cerner Nauka o danych biologicznych Cascading Hadoop — rozwiązanie na miarę wyzwań globalnych! Tom White — jeden z czołowych ekspertów w zakresie obsługi platformy Hadoop. Członek organizacji Apache Software Foundation, inżynier oprogramowania w firmie Cloudera.
Tanmay Deshpande
Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.
Quan Nguyen
JetBrain’s PyCharm is the most popular Integrated Development Environment (IDE) used by the Python community thanks to its numerous features that facilitate faster, more accurate, and more productive programming practices. However, the abundance of options and customizations can make PyCharm seem quite intimidating.Hands-on Application Development with PyCharm starts with PyCharm’s installation and configuration process, and systematically takes you through a number of its powerful features that can greatly improve your productivity. You’ll explore code automation, version control, graphical debugging/testing, management of virtual environments, and much more. Finally, you’ll delve into specific PyCharm features that support web development and data science, two of the fastest growing applications in Python programming. These include the integration of the Django framework as well as the extensive support for IPython and Jupyter Notebook.By the end of this PyCharm book, you will have gained extensive knowledge of the tool and be able to implement its features and make the most of its support for your projects.
David Dindi, Patrick D. Smith
Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world.Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games.By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.
Amita Kapoor, Hector Duran Lopez-Velarde
There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter.This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models.By the end of this book, you will be able to build smart AI-powered IoT apps with confidence.
Devangini Patel
With the emergence of big data and modern technologies, AI has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for AI in fields such as robotics, predictive analytics, finance, and more.In this book, you will understand what artificial intelligence is. It explains in detail basic search methods: Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for such problems. But these are not optimal in either space or time and efficient approaches in time and space will be explored. We will also understand how to formulate a problem, which involves looking at it and identifying its initial state, goal state, and the actions that are possible in each state. We also need to understand the data structures involved while implementing these search algorithms as they form the basis of search exploration. Finally, we will look into what a heuristic is as this decides the quality of one sub-solution over another and helps you decide which step to take.
Subhashini Tripuraneni, Charles Song
From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS.With this book, you’ll work through hands-on exercises and learn to use these services to solve real-world problems. You’ll even design, develop, monitor, and maintain machine and deep learning models on AWS.The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You’ll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you’ll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you’ll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning.By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle.