Database design and theory

The AI revolution is moving at a breakneck speed.

The use of dynamic models in the development of information systems is regarded by many researchers as a promising issue in design support.

The Final Volume of the Groundbreaking Trilogy on Agent-Based ModelingIn this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent_Zero.

This book presents advanced research on smart health technologies, focusing on the innovative transformations in diagnosis and treatment planning using medical imaging and data analysed by data science techniques.

Todays highly interactive websites pose a challenge for traditional SQL databasesthe ability to scale rapidly and serve loads of concurrent users.

Leverage the power of MongoDB 4.

System Requirements Analysis gives the professional systems engineer the tools to set up a proper and effective analysis of the resources, schedules and parts needed to successfully undertake and complete any large, complex project.

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.

Data has been a missing part of most academic curriculums for a long time, and we're all being affected.

Views are virtual tables.

Proceedings of the world renowned ERCOFTAC (International Symposium on Engineering Turbulence Modelling and Measurements).

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook DescriptionIn order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models.

Modelling and Precision Control of Systems with Hysteresis covers the piezoelectric and other smart materials that are increasingly employed as actuators in precision engineering, from scanning probe microscopes (SPMs) in life science and nano-manufacturing, to precision active optics in astronomy, including space laser communication, space imaging cameras, and the micro-electro-mechanical systems (MEMS).

The immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos.

Jump-start your career as a data scientist learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most.

Data mining techniques and algorithms are extensively used to build real-world applications.

Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant.

A quick start guide to visualize your Elasticsearch dataKey FeaturesYour hands-on guide to visualizing the Elasticsearch data as well as navigating the Elastic stackWork with different Kibana plugins and create effective machine learning jobs using KibanaBuild effective dashboards and reports without any hassleBook DescriptionThe Elastic Stack is growing rapidly and, day by day, additional tools are being added to make it more effective.

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person.

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences.

Offers researchers and teachers an introduction to clustering, with R code and real data to facilitate interaction with the concepts.

Get up and running with Oracle's premium cloud blockchain services and build distributed blockchain apps with easeKey FeaturesDiscover Hyperledger Fabric and its components, features, qualifiers, and architectureGet familiar with the Oracle Blockchain Platform and its unique featuresBuild Hyperledger Fabric-based business networks with Oracle's premium blockchain cloud serviceBook DescriptionHyperledger Fabric empowers enterprises to scale out in an unprecedented way, allowing organizations to build and manage blockchain business networks.

Big data involves the use of sophisticated analytics to make decisions based on large-scale data inputs.

Find, explore, and extract big data to transform into actionable insightsKey FeaturesPerform end-to-end data analysis-from exploration to visualizationReal-world examples, tasks, and interview queries to be a proficient data scientistUnderstand how SQL is used for big data processing using HiveQL and SparkSQLBook DescriptionSQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features.

It is a major challenge to migrate very large databases from one system, say for example, to transfer critical data from Oracle to SQL Server.

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark.

A comprehensive guide to developing neural network-based solutions using TensorFlow 2.

"e;THE BEST SQL BOOK FOR BEGINNERS IN 2020 - HANDS DOWN!

A modern, comprehensive 2010 overview providing an easy introduction for applied scientists who are not versed in mathematics.

Many organizations today analyze and share large, sensitive datasets about individuals.

Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes.

Leverage the power of MongoDB 4.

Data access is essential for serving the public good.

Until recently, many people thought big data was a passing fad.

Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and librariesKey FeaturesLearn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasksUnderstand and develop model-free and model-based algorithms for building self-learning agentsWork with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategiesBook DescriptionReinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements.

Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with JupyterKey FeaturesLearn how to use Jupyter 5.

Build efficient, high-performance & scalable systems to process large volumes of data with Apache IgniteKey FeaturesUnderstand Apache Ignite's in-memory technologyCreate High-Performance app components with IgniteBuild a real-time data streaming and complex event processing systemBook DescriptionApache Ignite is a distributed in-memory platform designed to scale and process large volume of data.

Jump-start your career as a data scientist learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most.

Data is at the center of many challenges in system design today.

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problemsKey FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning.

Data Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter.

For the past decade, e-mail has been the preferred method of internal communication in libraries.

A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial IntelligenceKey FeaturesLearn to build and run a big data application with sample codeExplore examples to implement activities that a big data architect performsUse Machine Learning and AI for structured and unstructured dataBook DescriptionThe big data architects are the "e;masters"e; of data, and hold high value in today's market.

Solve real-world business problems by learning how to create common industry key performance indicators and other calculations using DAX within Microsoft products such as Power BI, SQL Server, and Excel.

This book examines the use of big data in regenerative urban environment and how data helps in functional planning and design solutions.

Provides a practical overview of the most common database management tasks for those who need to manage data.

Build, design and improve advanced business intelligence solutions using Tableau's latest features, including Tableau Prep, Tableau Hyper, and Tableau ServerKey FeaturesMaster new features in Tableau 2019.