Windows IOCP vs Linux EPOLL Performance ComparisonSeungmo Koo
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1. The document compares the performance of IOCP and EPOLL for network I/O handling on Windows and Linux servers.
2. Testing showed that throughput was similar between IOCP and EPOLL, but IOCP had lower overall CPU usage without RSS/multi-queue enabled.
3. With RSS/multi-queue enabled on the NIC, CPU usage was nearly identical between IOCP and EPOLL.
Course Overview:
This course offers a comprehensive exploration of recommender systems, focusing on both theoretical foundations and practical applications. Through a combination of lectures, hands-on exercises, and real-world case studies, you will gain a deep understanding of the key principles, methodologies, and evaluation techniques that drive effective recommendation algorithms.
Course Objectives:
Acquire a solid understanding of recommender systems, including their significance and impact in various domains.
Explore different types of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches.
Study cutting-edge techniques, including deep learning, matrix factorization, and graph-based methods, for enhanced recommendation accuracy.
Gain hands-on experience with popular recommendation frameworks and libraries, and learn how to implement and evaluate recommendation models.
Investigate advanced topics in recommender systems, such as fairness, diversity, and explainability, and their ethical implications.
Analyze and discuss real-world case studies and research papers to gain insights into the challenges and future directions of recommender systems.
Course Structure:
Introduction to Recommender Systems
Collaborative Filtering Techniques
Content-Based Filtering and Hybrid Approaches
Matrix Factorization Methods
Deep Learning for Recommender Systems
Graph-Based Recommendation Approaches
Evaluation Metrics and Experimental Design
Ethical Considerations in Recommender Systems
Fairness, Diversity, and Explainability in Recommendations
Case Studies and Research Trends
Course Delivery:
The course will be delivered through a combination of lectures, interactive discussions, hands-on coding exercises, and group projects. You will have access to state-of-the-art resources, including relevant research papers, datasets, and software tools, to enhance your learning experience.
This document discusses using BigQuery and Dataflow for ETL processes. It explains loading raw data from databases into BigQuery, transforming the data with Dataflow, and writing the results. It also mentions pricing of $5 per terabyte for BigQuery storage and notes that Dataflow provides virtual CPUs and RAM. Finally, it includes a link about performing ETL from relational databases to BigQuery.
This document discusses starting a mobile app development company. It provides details on the company's founding in 2015, services offered such as mobile app development and information technology consulting. It also includes charts showing the company's growth, with revenue increasing from KRW 17,500 in January 2016 to KRW 62,000 by September 2017 as the number of employees grew from 2 to 15 over the same period. The document advocates that the company will continue achieving growth and success by focusing on customer satisfaction.
Course Overview:
This course offers a comprehensive exploration of recommender systems, focusing on both theoretical foundations and practical applications. Through a combination of lectures, hands-on exercises, and real-world case studies, you will gain a deep understanding of the key principles, methodologies, and evaluation techniques that drive effective recommendation algorithms.
Course Objectives:
Acquire a solid understanding of recommender systems, including their significance and impact in various domains.
Explore different types of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches.
Study cutting-edge techniques, including deep learning, matrix factorization, and graph-based methods, for enhanced recommendation accuracy.
Gain hands-on experience with popular recommendation frameworks and libraries, and learn how to implement and evaluate recommendation models.
Investigate advanced topics in recommender systems, such as fairness, diversity, and explainability, and their ethical implications.
Analyze and discuss real-world case studies and research papers to gain insights into the challenges and future directions of recommender systems.
Course Structure:
Introduction to Recommender Systems
Collaborative Filtering Techniques
Content-Based Filtering and Hybrid Approaches
Matrix Factorization Methods
Deep Learning for Recommender Systems
Graph-Based Recommendation Approaches
Evaluation Metrics and Experimental Design
Ethical Considerations in Recommender Systems
Fairness, Diversity, and Explainability in Recommendations
Case Studies and Research Trends
Course Delivery:
The course will be delivered through a combination of lectures, interactive discussions, hands-on coding exercises, and group projects. You will have access to state-of-the-art resources, including relevant research papers, datasets, and software tools, to enhance your learning experience.
This document discusses using BigQuery and Dataflow for ETL processes. It explains loading raw data from databases into BigQuery, transforming the data with Dataflow, and writing the results. It also mentions pricing of $5 per terabyte for BigQuery storage and notes that Dataflow provides virtual CPUs and RAM. Finally, it includes a link about performing ETL from relational databases to BigQuery.
This document discusses starting a mobile app development company. It provides details on the company's founding in 2015, services offered such as mobile app development and information technology consulting. It also includes charts showing the company's growth, with revenue increasing from KRW 17,500 in January 2016 to KRW 62,000 by September 2017 as the number of employees grew from 2 to 15 over the same period. The document advocates that the company will continue achieving growth and success by focusing on customer satisfaction.
[???, kosena] Auto ML, H2O.ai? ?????? AI ?? ??kosena
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H2O.ai, founded in 2012 in Silicon Valley, aims to enhance healthcare through AI, boasting a strong community and significant funding, totaling $152 million. The organization provides a range of AI solutions tailored for the healthcare sector, including applications in pharma, provider operations, and disease management. Recognized for its innovations, H2O.ai strives to democratize AI and has established partnerships with notable healthcare companies while emphasizing the importance of explainable AI.
The H2O.ai Insurance Use Case Catalog outlines various AI applications tailored for the insurance sector, including pricing, underwriting, claims management, fraud detection, and customer experience optimization. Each use case provides objectives, outcomes, business value, and specific AI methodologies, along with tools designed to automate processes and enhance decision-making. Key features include improved accuracy in claims processing, customer retention strategies, and advanced data analytics to foresee risks and market behaviors.
[???, kosena] Auto ML, H2O.ai? ???? AI ?? ??kosena
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The document discusses the shift towards intelligent manufacturing, highlighting the importance of AI in enhancing operational efficiency and decision-making. It presents case studies and examples of successful AI implementations across various industries, outlining specific use cases and the potential value they can drive. Additionally, it emphasizes the H2O.ai cloud platform's capabilities in democratizing AI and enabling organizations to achieve significant returns on investment through AI transformation.
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Written By ??? ??, kosena21@naver.com, 010-9338-6400