Stay Ahead of the Curve: Latest Insights & Trending Topics

Tag: Graphs

  • Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs b

    Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs b



    Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs b

    Price : 178.34

    Ends on : N/A

    View on eBay
    Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs

    In the world of data science, understanding the provenance of data is crucial for ensuring its reliability and accuracy. Provenance refers to the origin or source of data, as well as the processes and transformations that data has undergone.

    Data models play a key role in capturing and representing the structure and relationships within a dataset. By understanding the provenance of a data model, data scientists can trace back the lineage of data, identify errors or inconsistencies, and ensure the integrity of their analyses.

    However, data models alone may not provide a complete picture of the provenance of data. Context-aware knowledge graphs, which capture not only the structure of data but also the context in which it was created and used, are becoming increasingly important in data science.

    By incorporating provenance information into knowledge graphs, data scientists can gain valuable insights into the history and evolution of data, as well as the relationships between different datasets. This can help in making more informed decisions, improving data quality, and ensuring compliance with regulations such as GDPR.

    In this post, we will explore the importance of provenance in data science, the role of data models in capturing provenance, and the benefits of using context-aware knowledge graphs to enhance our understanding of data. Stay tuned for more insights and practical tips on how to leverage provenance in your data science projects.
    #Provenance #Data #Science #Data #Models #ContextAware #Knowledge #Graphs, Intelligent data infrastructure

  • Wireless Hygrometer Thermometer – Remote Monitoring, Temp Humidity Graphs

    Wireless Hygrometer Thermometer – Remote Monitoring, Temp Humidity Graphs



    Wireless Hygrometer Thermometer – Remote Monitoring, Temp Humidity Graphs

    Price : 25.99

    Ends on : N/A

    View on eBay
    Are you looking for a convenient way to monitor the temperature and humidity levels in your home or office? Look no further than the Wireless Hygrometer Thermometer!

    With this innovative device, you can easily track and monitor the temperature and humidity levels from anywhere using your smartphone. Simply download the companion app, connect the hygrometer thermometer to your Wi-Fi network, and you can start receiving real-time updates and notifications.

    But that’s not all – the Wireless Hygrometer Thermometer also allows you to view historical data and track trends with easy-to-read temperature and humidity graphs. This feature is perfect for keeping an eye on any changes in your environment and making adjustments as needed.

    Say goodbye to manual monitoring and hello to convenient remote monitoring with the Wireless Hygrometer Thermometer. Keep your space comfortable and healthy with this smart and reliable device.
    #Wireless #Hygrometer #Thermometer #Remote #Monitoring #Temp #Humidity #Graphs, Remote Monitoring

  • Portable 3-in-1 Carbon Monoxide detectors air Quality Monitor co Detector with Temp Humidity Monitor -Peak Recording graphs Battery Adjustable Alarm Trigger- Indoor/Outdoor Travel rv Camping

    Portable 3-in-1 Carbon Monoxide detectors air Quality Monitor co Detector with Temp Humidity Monitor -Peak Recording graphs Battery Adjustable Alarm Trigger- Indoor/Outdoor Travel rv Camping


    Price: $43.99
    (as of Dec 21,2024 05:24:27 UTC – Details)


    Product Description

    carbon monoxide detector plug in

    carbon monoxide detector plug in

    battery powered carbon monoxide detector

    co detector 10 year battery

    co detector 10 year battery

    battery operated carbon monoxide detector

    plug in carbon monoxide detectors

    plug in carbon monoxide detectors

    digital carbon monoxide detector

    battery carbon monoxide detector

    battery carbon monoxide detector

    car carbon monoxide detector

    carbon monoxide detectors

    carbon monoxide detectors

    low level carbon monoxide detectors

    【Reliable】The carbon monoxide detector features a Japan-made electrochemical CO sensor (0-1000PPM) for precise and reliable readings. The display always-on mode allows real-time monitoring without additional operation, ideal for driving.
    【Customizable Alarms】Adjust the carbon monoxide alarm triggers(CO: 10~200PPM / CO2: 801~3000PPM) to meet WHO, EPA, ASHRAE, OSHA, and NIOSH CO exposure recommendations. gas detector customize alerts to suit your specific needs with this low-level carbon monoxide detector.
    【Peak Recording Graphs】The gas detector 8 hours Peak recording graphs allow you to track and analyze carbon monoxide levels over time for enhanced safety.
    【Easy Use】Compact and portable designed gas detector portable, air quality monitor measuring just 2.21 x 0.79 x 3.52 inches, with a lanyard and wall mount included for easy installation.
    【Adjustable Volume】The carbon monoxide detectors portable and CO2 detector features adjustable volume settings up to 85 dB, ensuring you hear the alarm even in noisy environments for your safety. For added protection, the device will sound an alarm if CO levels exceed 200PPM or CO2 levels surpass 3000PPM, even in silent mode. You can silence this specific alarm by pressing the left button.
    【Long-Lifespan】The battery carbon monoxide detector alarm’s lifespan up to 7 years, long-lasting 2000mAh battery offers up to 120 hours in power-saving mode, ensuring continuous protection on the go.
    【How to Test It 】Take the carbon monoxide meter close to the smoke produced by cigarettes or burning paper, the CO reading will rise rapidly. Don’t test it by car exhaust or airy place, when CO concentration is too low to be detected or no CO, the reading shows 0 PPM.
    【Shopping Confidence】Your satisfaction is our top priority. The gas detector feature high-precision, stable, and sensitive sensors, along with a high-definition screen and large-capacity battery to enhance your experience, and we offer lifetime technical support, if you have any questions, please just contact us via Amazon Customer Service, and we will respond within 24 hours.


    Are you looking for a reliable carbon monoxide detector and air quality monitor for your indoor or outdoor activities? Look no further than our Portable 3-in-1 Carbon Monoxide detector with Temp Humidity Monitor!

    This compact and versatile device not only detects carbon monoxide levels but also monitors the temperature and humidity of your surroundings. With peak recording graphs and an adjustable alarm trigger, you can easily track and respond to any changes in air quality.

    Perfect for indoor use in your home, office, or RV, as well as outdoor activities like camping and travel, this detector is a must-have for anyone concerned about their safety and well-being. Plus, with a long-lasting battery, you can rely on this device to keep you protected wherever you go.

    Don’t take any chances with your health – invest in a Portable 3-in-1 Carbon Monoxide detector today!
    #Portable #3in1 #Carbon #Monoxide #detectors #air #Quality #Monitor #Detector #Temp #Humidity #Monitor #Peak #Recording #graphs #Battery #Adjustable #Alarm #Trigger #IndoorOutdoor #Travel #Camping

  • Applied Deep Learning on Graphs: Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures

    Applied Deep Learning on Graphs: Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures


    Price: $38.99
    (as of Dec 18,2024 08:05:46 UTC – Details)




    ASIN ‏ : ‎ B0CTXM42WG
    Publisher ‏ : ‎ Packt Publishing; 1st edition (December 27, 2024)
    Publication date ‏ : ‎ December 27, 2024
    Language ‏ : ‎ English
    File size ‏ : ‎ 7108 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled


    Graph data is widely used in various domains such as social networks, recommendation systems, bioinformatics, and more. With the advancement of deep learning techniques, researchers and practitioners have been exploring ways to leverage graph data to generate impactful insights. In this post, we will discuss how applied deep learning on graphs can help in solving complex problems and improving decision-making processes.

    One of the key challenges in applying deep learning on graphs is the structure of the data. Unlike traditional data formats such as images or text, graph data is inherently non-Euclidean and lacks a fixed grid-like structure. This makes it challenging to apply traditional deep learning architectures directly to graph data.

    To address this challenge, specialized deep learning architectures have been developed specifically for graph data. Graph neural networks (GNNs) are one such architecture that has gained popularity in recent years. GNNs are designed to operate on graph-structured data and can effectively capture the relationships between nodes in a graph.

    By leveraging GNNs, researchers and practitioners can extract meaningful patterns and insights from graph data. For example, in social network analysis, GNNs can be used to predict friendships or identify influential nodes in a network. In recommendation systems, GNNs can help in improving the accuracy of recommendations by considering the relationships between items and users.

    Furthermore, GNNs can also be combined with other deep learning techniques such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to create more powerful models for graph data. This enables researchers to tackle even more complex problems and generate impactful insights.

    In conclusion, applied deep learning on graphs using specialized architectures such as GNNs has the potential to revolutionize various domains by leveraging the rich information present in graph data. By effectively capturing the relationships between nodes in a graph, deep learning models can generate impactful insights and improve decision-making processes. As more research and development efforts are focused on this area, we can expect to see even more innovative applications of deep learning on graphs in the future.
    #Applied #Deep #Learning #Graphs #Leveraging #Graph #Data #Generate #Impact #Specialized #Deep #Learning #Architectures

  • Exploiting Linked Data and Knowledge Graphs in Large Organisations

    Exploiting Linked Data and Knowledge Graphs in Large Organisations



    Exploiting Linked Data and Knowledge Graphs in Large Organisations

    Price : 208.39

    Ends on : N/A

    View on eBay
    In today’s digital age, large organisations are turning to linked data and knowledge graphs to unlock the full potential of their data. By exploiting these technologies, companies can streamline operations, improve decision-making, and drive innovation.

    Linked data refers to the practice of connecting related data sets together, enabling organizations to easily navigate and explore their data in a more holistic manner. Knowledge graphs, on the other hand, are a way to represent knowledge in a structured format, allowing for better understanding and reasoning about relationships between different pieces of information.

    By leveraging linked data and knowledge graphs, large organisations can benefit in numerous ways. For starters, these technologies enable better data integration, allowing companies to break down data silos and gain a more comprehensive view of their information. This, in turn, leads to improved data quality, consistency, and accuracy.

    Furthermore, linked data and knowledge graphs facilitate more intelligent data analytics and machine learning applications. By connecting disparate data sources and relationships, organisations can uncover valuable insights and patterns that were previously hidden. This can lead to more informed decision-making, enhanced business strategies, and ultimately, competitive advantage.

    Additionally, linked data and knowledge graphs can improve collaboration within organisations. By providing a common framework for sharing and accessing information, these technologies enable employees to work more effectively across departments and teams. This can foster innovation, creativity, and knowledge sharing throughout the organisation.

    In conclusion, exploiting linked data and knowledge graphs can be a game-changer for large organisations looking to harness the power of their data. By breaking down data barriers, enabling better analytics, and fostering collaboration, companies can drive efficiency, innovation, and success in today’s data-driven world.
    #Exploiting #Linked #Data #Knowledge #Graphs #Large #Organisations

  • Deep Learning on Graphs

    Deep Learning on Graphs



    Deep Learning on Graphs

    Price : 68.78

    Ends on : N/A

    View on eBay
    Deep learning on graphs is a fascinating and rapidly growing field that combines the power of deep learning with the complexity and structure of graph data. Graphs are used to represent relationships and connections between entities, such as social networks, biological networks, and recommendation systems.

    Deep learning on graphs involves developing algorithms and models that can effectively process and analyze graph data. These algorithms can learn from the structure and features of the graph to make predictions, classify nodes, and detect patterns.

    Some popular techniques for deep learning on graphs include graph neural networks (GNNs), graph convolutional networks (GCNs), and graph attention networks (GATs). These models can capture the topology of the graph and leverage information from neighboring nodes to make predictions.

    Applications of deep learning on graphs are diverse and include social network analysis, drug discovery, fraud detection, and recommendation systems. By leveraging the power of deep learning on graph data, researchers and practitioners can uncover valuable insights and make better decisions in various domains.

    Stay tuned for more updates and insights on deep learning on graphs!
    #Deep #Learning #Graphs

  • Deep Learning on Graphs

    Deep Learning on Graphs


    Price: $62.99
    (as of Dec 17,2024 08:54:47 UTC – Details)



    Deep learning on graphs is a rapidly growing field within machine learning that focuses on leveraging the structure and relationships present in graph data to make predictions and extract insights. Graphs are a powerful way to represent complex relationships and dependencies between entities, making them well-suited for modeling real-world systems such as social networks, biological networks, and recommendation systems.

    One of the key challenges in applying deep learning to graph data is how to effectively capture the intricate relationships and dependencies present in the graph. Traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are designed for grid-like data such as images and sequences, and may not be directly applicable to graphs.

    To address this challenge, researchers have developed a variety of graph neural network (GNN) architectures that are specifically designed to operate on graph-structured data. These GNNs typically aggregate information from neighboring nodes in a graph to update the node representations, allowing the model to capture the global structure and local interactions present in the graph.

    Some popular GNN architectures include Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE. These models have been successfully applied to a wide range of tasks, including node classification, link prediction, and graph classification.

    Overall, deep learning on graphs holds great promise for enabling powerful and interpretable models for analyzing and making predictions on complex relational data. As research in this field continues to advance, we can expect to see even more innovative applications and breakthroughs in leveraging graph data for machine learning tasks.
    #Deep #Learning #Graphs

  • Enterprise Intelligence: Bridging the Gaps between Wisdom, Business Intelligence, Knowledge Graphs, and Artificial Intelligence

    Enterprise Intelligence: Bridging the Gaps between Wisdom, Business Intelligence, Knowledge Graphs, and Artificial Intelligence


    Price: $69.95 – $53.99
    (as of Dec 16,2024 10:33:46 UTC – Details)




    Publisher ‏ : ‎ Technics Publications (June 21, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 528 pages
    ISBN-10 ‏ : ‎ 1634624513
    ISBN-13 ‏ : ‎ 978-1634624510
    Item Weight ‏ : ‎ 2.29 pounds
    Dimensions ‏ : ‎ 8 x 1.19 x 10 inches


    Enterprise Intelligence: Bridging the Gaps between Wisdom, Business Intelligence, Knowledge Graphs, and Artificial Intelligence

    In today’s rapidly evolving business landscape, organizations are constantly seeking ways to harness data and turn it into actionable insights. This is where Enterprise Intelligence comes into play, serving as the bridge between various disciplines such as wisdom, business intelligence, knowledge graphs, and artificial intelligence.

    Wisdom, often considered the highest form of knowledge, involves the ability to make sound judgments and decisions based on experience and understanding. Business Intelligence, on the other hand, focuses on the collection, analysis, and presentation of data to inform decision-making within organizations.

    Knowledge Graphs take this a step further by organizing information in a way that allows for better understanding and discovery of relationships between data points. Artificial Intelligence, with its ability to process vast amounts of data and learn from it, adds another layer of complexity to the mix.

    Enterprise Intelligence integrates these different disciplines, leveraging the strengths of each to provide organizations with a comprehensive view of their data and actionable insights. By combining wisdom with the analytical power of business intelligence, knowledge graphs, and artificial intelligence, enterprises can make more informed decisions and drive growth and innovation.

    In essence, Enterprise Intelligence serves as the glue that brings together the various pieces of the data puzzle, enabling organizations to unlock the full potential of their data and drive success in an increasingly competitive business environment.
    #Enterprise #Intelligence #Bridging #Gaps #Wisdom #Business #Intelligence #Knowledge #Graphs #Artificial #Intelligence

  • Mastering Deep Learning with PyTorch a Practical Guide: Build Advanced Neural Networks and AI Models Using Dynamic Computation Graphs

    Mastering Deep Learning with PyTorch a Practical Guide: Build Advanced Neural Networks and AI Models Using Dynamic Computation Graphs


    Price: $7.92
    (as of Dec 16,2024 08:54:52 UTC – Details)




    ASIN ‏ : ‎ B0DJSRL3W8
    Publication date ‏ : ‎ October 9, 2024
    Language ‏ : ‎ English
    File size ‏ : ‎ 1084 KB
    Simultaneous device usage ‏ : ‎ Unlimited
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 441 pages


    Are you ready to take your deep learning skills to the next level? Look no further than “Mastering Deep Learning with PyTorch: A Practical Guide.” In this comprehensive guide, you will learn how to build advanced neural networks and AI models using dynamic computation graphs.

    PyTorch is a powerful deep learning framework that enables you to easily create complex neural networks and train them on large datasets. With its dynamic computation graph feature, PyTorch allows for greater flexibility and creativity in designing your models.

    This book will walk you through the fundamentals of deep learning and PyTorch, and then dive into more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. You will also learn how to optimize your models, troubleshoot common issues, and deploy them in real-world applications.

    Whether you are a beginner looking to expand your knowledge or an experienced deep learning practitioner seeking to enhance your skills, “Mastering Deep Learning with PyTorch” is the ultimate resource for mastering the art of building advanced neural networks and AI models. Get your copy today and start creating cutting-edge solutions with PyTorch.
    #Mastering #Deep #Learning #PyTorch #Practical #Guide #Build #Advanced #Neural #Networks #Models #Dynamic #Computation #Graphs