Play with Kaggle
•
1 like•2,685 views
The document discusses Kaggle, an online platform for predictive modeling competitions. It describes a dataset from Yandex containing query, click, and session data for evaluating search engine rankings. The goal is to build a model that can better predict click probabilities and sort URLs than Yandex's current algorithm. The proposed approach involves: 1) reshaping the data by user sessions; 2) cross-validating on a test set; 3) adding new user and query features; and 4) using logistic regression or random forests to predict click probabilities and improve the rankings.
Report
Share
Report
Share
Download to read offline
Recommended
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.
Kaggle: Crowd Sourcing for Data Analytics
These slides use concepts from my (Jeff Funk) course entitled Biz Models for Hi-Tech Products to analyze the business model for Kaggle’s Crowd Sourcing Service for Data Analytics. Kaggle connects data scientists with organizations who have problems related to data analysis. Kaggle helps organizations define their data analytic problems, present them to data scientists, and organize and evaluate competitions between data analytic solutions. Its data ensemble technique also evaluates the effectiveness of the various solutions. These slides describe the specific value proposition for organizations and data scientists and other aspects of the business model such as the method of value capture, scope of activities, and method of strategic control.
Winning Kaggle 101: Introduction to Stacking
This document provides an introduction to stacking, an ensemble machine learning method. Stacking involves training a "metalearner" to optimally combine the predictions from multiple "base learners". The stacking algorithm was developed in the 1990s and improved upon with techniques like cross-validation and the "Super Learner" which combines models in a way that is provably asymptotically optimal. H2O implements an efficient stacking method called H2O Ensemble which allows for easily finding the best combination of algorithms like GBM, DNNs, and more to improve predictions.
Kaggle Winning Solution Xgboost algorithm -- Let us learn from its author
This document provides an overview of XGBoost, an open-source gradient boosting framework. It begins with introductions to machine learning algorithms and XGBoost specifically. The document then walks through using XGBoost with R, including loading data, running models, cross-validation, and prediction. It discusses XGBoost's use in winning the Higgs Boson machine learning competition and provides code to replicate its solution. Finally, it briefly covers XGBoost's model specification and training objectives.
Winning data science competitions, presented by Owen Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
Tips for data science competitions
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
Florian Douetteau @ Dataiku
The document describes a Kaggle competition to personalize web search results using historical search engine logs. A team called Dataiku used supervised learning techniques like random forests and LambdaMART gradient boosted trees to re-rank web search results for new queries based on features from past user search behavior, achieving a 1st place result. Their approach involved prototyping with fast methods, optimizing models with boosting, and being systematic about feature selection and validation.
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
This is a presentation made on the 13th August 2014 at the SF Data Mining Meetup at Trulia. It's about Dataiku and the Kaggle Personalized Web Search Ranking challenge sponsored by Yandex
Recommended
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.
Kaggle: Crowd Sourcing for Data Analytics
These slides use concepts from my (Jeff Funk) course entitled Biz Models for Hi-Tech Products to analyze the business model for Kaggle’s Crowd Sourcing Service for Data Analytics. Kaggle connects data scientists with organizations who have problems related to data analysis. Kaggle helps organizations define their data analytic problems, present them to data scientists, and organize and evaluate competitions between data analytic solutions. Its data ensemble technique also evaluates the effectiveness of the various solutions. These slides describe the specific value proposition for organizations and data scientists and other aspects of the business model such as the method of value capture, scope of activities, and method of strategic control.
Winning Kaggle 101: Introduction to Stacking
This document provides an introduction to stacking, an ensemble machine learning method. Stacking involves training a "metalearner" to optimally combine the predictions from multiple "base learners". The stacking algorithm was developed in the 1990s and improved upon with techniques like cross-validation and the "Super Learner" which combines models in a way that is provably asymptotically optimal. H2O implements an efficient stacking method called H2O Ensemble which allows for easily finding the best combination of algorithms like GBM, DNNs, and more to improve predictions.
Kaggle Winning Solution Xgboost algorithm -- Let us learn from its author
This document provides an overview of XGBoost, an open-source gradient boosting framework. It begins with introductions to machine learning algorithms and XGBoost specifically. The document then walks through using XGBoost with R, including loading data, running models, cross-validation, and prediction. It discusses XGBoost's use in winning the Higgs Boson machine learning competition and provides code to replicate its solution. Finally, it briefly covers XGBoost's model specification and training objectives.
Winning data science competitions, presented by Owen Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
Tips for data science competitions
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
Florian Douetteau @ Dataiku
The document describes a Kaggle competition to personalize web search results using historical search engine logs. A team called Dataiku used supervised learning techniques like random forests and LambdaMART gradient boosted trees to re-rank web search results for new queries based on features from past user search behavior, achieving a 1st place result. Their approach involved prototyping with fast methods, optimizing models with boosting, and being systematic about feature selection and validation.
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
This is a presentation made on the 13th August 2014 at the SF Data Mining Meetup at Trulia. It's about Dataiku and the Kaggle Personalized Web Search Ranking challenge sponsored by Yandex
Welcome Webinar Slides
This document provides an agenda and overview for a Sumo Logic webinar training session. The agenda includes sections on data collection, search and analysis, and visualizing and monitoring. It discusses Sumo Logic's analytics platform and data flow. It also provides instructions for logging into a training environment and demonstrates examples of searching log data and creating dashboards and alerts.
SEMNE Google Analytics Master Class - 15 Oct 2014
This document provides an overview and agenda for a Google Analytics training session. It covers key topics like SEO and analytics, new features in Universal Analytics like custom dimensions and user ID tracking, and analysis techniques. Definitions of important analytics terms are also included. The training will explain how Google Analytics works and the different types of reports available to analyze audience, acquisition, behavior, and other metrics.
Recsys2016 Tutorial by Xavier and Deepak
This is part 1 of the tutorial Xavier and Deepak gave at Recsys 2016 this year. You can find the second part http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
Cloudera Movies Data Science Project On Big Data
The document describes a data science project conducted on streaming log data from Cloudera Movies, an online streaming video service. The goals of the project were to understand which user accounts are used most by younger viewers, segment user sessions to improve site usability, and build a recommendation engine. Key steps included exploring and cleaning the data, classifying users as children or adults using a SimRank approach, clustering user sessions to identify behavior patterns, and predicting user ratings through user-user and item-item similarity models to build a recommendation system. Accuracy of 99.64% was achieved in classifying users.
Google Analytics
This document provides an overview of Google Analytics. It discusses why websites should use analytics and how to install Google Analytics. Key definitions are explained, like unique visitors, visits, bounce rate, etc. Case studies are presented showing how analytics was used to measure SEO performance and analyze complex user interactions on websites. Resources for learning more about analytics and SEO are also listed. The document concludes by explaining how Google Analytics works and the cookies it uses to track visitors.
Iterative Methodology for Personalization Models Optimization
Describing the iterative process of optimizing personalized content recommendation models, starting from user preferences optimization to a spark-based large-scale modular machine learning framework.
moma-django overview --> Django + MongoDB: building a custom ORM layer
moma-django is a MongoDB manager for Django. It provides native Django ORM support for MongoDB documents, including the query API and the admin interface. It was developed as a part of two commercial products and released as an open source. In the talk we will review the motivation behind its developments, its features and go through 2-3 examples of how to use some of the features: migrating an existing model, advanced queries and the admin interface. If time permits we will discuss unit testing and south migrations.
Please find the video at: http://www.youtube.com/watch?v=cxQKTDLjb-w
Also check out: https://twitter.com/gadioren and www.ITculate.io
Supercharging your Organic CTR
This document discusses supercharging organic click-through rate (CTR) through the use of JSON for Linked Data (JSON-LD). It covers:
1. What JSON-LD is and the benefits it provides like rich snippets and action buttons
2. Different implementation methods like using WordPress plugins or Google Tag Manager
3. Examples of JSON-LD markup for things like products, reviews, and local businesses
4. Testing and monitoring the impact on organic CTR before and after implementing JSON-LD
Владимир Гулин, Mail.Ru Group, Learning to rank using clickthrough data
Владимир в своём докладе рассказал о том, как можно использовать «кликовые» данные для улучшения ранжирования в поисковых системах
kdd2015
This document discusses scaling machine learning and statistics for web applications like recommendations, search, and advertising. It describes LinkedIn's vision of connecting the world's professionals to make them more productive and successful. It discusses using algorithmic match-making via machine learning and data mining to connect talent with opportunities at scale. This involves continuously learning from historical user data and interactions through machine learning models and experiments to power services like search, personalized content recommendations, and advertising.
Search Engine Optimization
This document discusses a proposed search engine optimization (SEO) system. It includes an abstract describing SEO and its goals. The scope section discusses how SEO is commonly used to improve search engine rankings. The proposed system would allow users to search for content by keyword and refine results. It would display search results across different formats. The system requirements, design, testing approach, and screenshots are also outlined. In conclusion, the document states that SEO is an ongoing process that requires constant adaptation to changes in technology and search engine algorithms.
How can a data layer help my seo
This document discusses how a JavaScript data layer can help with SEO by providing structured data to search engines. It covers:
1. Using HTML, microdata, and a JavaScript data layer to provide different types of structured data. A JavaScript data layer allows providing data not accessible to robots through HTML alone.
2. Benefits of a data layer for SEO include increased organic click-through rate, better SEO analysis, and enabling dynamic remarketing in AdWords.
3. Examples of setting up a data layer using Google Tag Manager, JSON-LD syntax, and pinging Googlebot to re-crawl pages to index the new structured data.
Mozilla Foundation Metrics - presentation to engineers
@rossbruniges and I talked with our fellow Mozilla Foundation engineers and development teams about getting the data for building a data driven operation using statsd, graphite, geckoboard, google analytics, and newrelic.
Profiler for Smartphone Users Interests Using Modified Hierarchical Agglomera...
1) The document proposes a modified hierarchical agglomerative clustering algorithm to profile smartphone users' interests based on their browsing history.
2) It collects browsing history data from 30 student participants over 1 month and extracts URLs to analyze user interests.
3) The algorithm clusters URLs into categories and calculates the degree of interest for each category to create user profiles.
4) Experimental results show the proposed algorithm outperforms C4.5 in execution time and accuracy for profiling users' interests based on browsing history.
A new algorithm for inferring user search goals with feedback sessions
The document proposes a new algorithm to infer user search goals from query logs by analyzing feedback sessions constructed from user click-through data. It clusters feedback sessions to discover different user search goals for a query. It generates "pseudo-documents" to better represent feedback sessions for clustering. It also proposes a new evaluation metric "Classified Average Precision" to assess goal inference performance. The algorithm is tested on a commercial search engine's query logs and is shown to effectively infer user search goals.
Recommender Systems @ Scale - PyData 2019
Serving tens of billions of personalized recommendations a day under a latency of 30 milliseconds is a challenge. In this talk I'll share our algorithmic architecture, including its Spark-based offline layer, and its Elasticsearch-based serving layer, that enable running complex models under difficult scale constrains and shorten the cycle between research and production.
Sonya Liberman leads the Personalization team @ Outbrain's Recommendations group, developing large-scale machine learning algorithms for Outbrain's content recommendations platform serving tens of billions real-time recommendations a day. She specializes in Information Retrieval, Machine Learning, and Computational Linguistics. Before joining Outbrain, she led the Research and Algorithms @ ConvertMedia (acquired by Taboola). She holds an MSc in Computer Science and a BSc in Computer Science and Computational Biology.
This invited talk was given at PyData Meetup, April 2019
https://www.meetup.com/PyData-Tel-Aviv/
A new algorithm for inferring user search goals with feedback sessions
This document proposes a new algorithm to infer user search goals from query logs. It clusters feedback sessions from click-through data to discover different goals for an ambiguous query. It generates pseudo-documents to represent sessions for clustering. The algorithm evaluates inferred goals using a new metric called Classified Average Precision. Experiments on a commercial search engine validate the method effectively discovers user search goals.
Find and be Found: Information Retrieval at LinkedIn
Shakti Sinha and Daniel Tunkelang discuss how LinkedIn's search functionality works. They explain that LinkedIn search is personalized based on a user's profile and network. Query understanding involves tagging queries to determine entity types like people, companies, or skills. Ranking is also personalized using machine learning models trained on search logs to determine relevance for a specific user's query. The system aims to provide both globally and personally relevant results, as about two-thirds of clicks come from out of a user's network.
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
This document summarizes an algorithmic digital attribution model implemented using Spark. It begins with an overview of digital attribution and how algorithmic models can determine attribution weights. It then discusses how the model was implemented using Spark, including data processing, model building, and attribution calculations. Key lessons learned are around memory management, iterative computation, and error handling when working with Spark.
Intro to Google Analytics and Google AdWords (March 19 2013)
as part of the Chester County Marketing Group's monthly meetings, Jeff Tincher gave a presentation about Google AdWords and Analytics and how a small business could value from both.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
More Related Content
Similar to Play with Kaggle
Welcome Webinar Slides
This document provides an agenda and overview for a Sumo Logic webinar training session. The agenda includes sections on data collection, search and analysis, and visualizing and monitoring. It discusses Sumo Logic's analytics platform and data flow. It also provides instructions for logging into a training environment and demonstrates examples of searching log data and creating dashboards and alerts.
SEMNE Google Analytics Master Class - 15 Oct 2014
This document provides an overview and agenda for a Google Analytics training session. It covers key topics like SEO and analytics, new features in Universal Analytics like custom dimensions and user ID tracking, and analysis techniques. Definitions of important analytics terms are also included. The training will explain how Google Analytics works and the different types of reports available to analyze audience, acquisition, behavior, and other metrics.
Recsys2016 Tutorial by Xavier and Deepak
This is part 1 of the tutorial Xavier and Deepak gave at Recsys 2016 this year. You can find the second part http://www.slideshare.net/xamat/recsys-2016-tutorial-lessons-learned-from-building-reallife-recommender-systems
Cloudera Movies Data Science Project On Big Data
The document describes a data science project conducted on streaming log data from Cloudera Movies, an online streaming video service. The goals of the project were to understand which user accounts are used most by younger viewers, segment user sessions to improve site usability, and build a recommendation engine. Key steps included exploring and cleaning the data, classifying users as children or adults using a SimRank approach, clustering user sessions to identify behavior patterns, and predicting user ratings through user-user and item-item similarity models to build a recommendation system. Accuracy of 99.64% was achieved in classifying users.
Google Analytics
This document provides an overview of Google Analytics. It discusses why websites should use analytics and how to install Google Analytics. Key definitions are explained, like unique visitors, visits, bounce rate, etc. Case studies are presented showing how analytics was used to measure SEO performance and analyze complex user interactions on websites. Resources for learning more about analytics and SEO are also listed. The document concludes by explaining how Google Analytics works and the cookies it uses to track visitors.
Iterative Methodology for Personalization Models Optimization
Describing the iterative process of optimizing personalized content recommendation models, starting from user preferences optimization to a spark-based large-scale modular machine learning framework.
moma-django overview --> Django + MongoDB: building a custom ORM layer
moma-django is a MongoDB manager for Django. It provides native Django ORM support for MongoDB documents, including the query API and the admin interface. It was developed as a part of two commercial products and released as an open source. In the talk we will review the motivation behind its developments, its features and go through 2-3 examples of how to use some of the features: migrating an existing model, advanced queries and the admin interface. If time permits we will discuss unit testing and south migrations.
Please find the video at: http://www.youtube.com/watch?v=cxQKTDLjb-w
Also check out: https://twitter.com/gadioren and www.ITculate.io
Supercharging your Organic CTR
This document discusses supercharging organic click-through rate (CTR) through the use of JSON for Linked Data (JSON-LD). It covers:
1. What JSON-LD is and the benefits it provides like rich snippets and action buttons
2. Different implementation methods like using WordPress plugins or Google Tag Manager
3. Examples of JSON-LD markup for things like products, reviews, and local businesses
4. Testing and monitoring the impact on organic CTR before and after implementing JSON-LD
Владимир Гулин, Mail.Ru Group, Learning to rank using clickthrough data
Владимир в своём докладе рассказал о том, как можно использовать «кликовые» данные для улучшения ранжирования в поисковых системах
kdd2015
This document discusses scaling machine learning and statistics for web applications like recommendations, search, and advertising. It describes LinkedIn's vision of connecting the world's professionals to make them more productive and successful. It discusses using algorithmic match-making via machine learning and data mining to connect talent with opportunities at scale. This involves continuously learning from historical user data and interactions through machine learning models and experiments to power services like search, personalized content recommendations, and advertising.
Search Engine Optimization
This document discusses a proposed search engine optimization (SEO) system. It includes an abstract describing SEO and its goals. The scope section discusses how SEO is commonly used to improve search engine rankings. The proposed system would allow users to search for content by keyword and refine results. It would display search results across different formats. The system requirements, design, testing approach, and screenshots are also outlined. In conclusion, the document states that SEO is an ongoing process that requires constant adaptation to changes in technology and search engine algorithms.
How can a data layer help my seo
This document discusses how a JavaScript data layer can help with SEO by providing structured data to search engines. It covers:
1. Using HTML, microdata, and a JavaScript data layer to provide different types of structured data. A JavaScript data layer allows providing data not accessible to robots through HTML alone.
2. Benefits of a data layer for SEO include increased organic click-through rate, better SEO analysis, and enabling dynamic remarketing in AdWords.
3. Examples of setting up a data layer using Google Tag Manager, JSON-LD syntax, and pinging Googlebot to re-crawl pages to index the new structured data.
Mozilla Foundation Metrics - presentation to engineers
@rossbruniges and I talked with our fellow Mozilla Foundation engineers and development teams about getting the data for building a data driven operation using statsd, graphite, geckoboard, google analytics, and newrelic.
Profiler for Smartphone Users Interests Using Modified Hierarchical Agglomera...
1) The document proposes a modified hierarchical agglomerative clustering algorithm to profile smartphone users' interests based on their browsing history.
2) It collects browsing history data from 30 student participants over 1 month and extracts URLs to analyze user interests.
3) The algorithm clusters URLs into categories and calculates the degree of interest for each category to create user profiles.
4) Experimental results show the proposed algorithm outperforms C4.5 in execution time and accuracy for profiling users' interests based on browsing history.
A new algorithm for inferring user search goals with feedback sessions
The document proposes a new algorithm to infer user search goals from query logs by analyzing feedback sessions constructed from user click-through data. It clusters feedback sessions to discover different user search goals for a query. It generates "pseudo-documents" to better represent feedback sessions for clustering. It also proposes a new evaluation metric "Classified Average Precision" to assess goal inference performance. The algorithm is tested on a commercial search engine's query logs and is shown to effectively infer user search goals.
Recommender Systems @ Scale - PyData 2019
Serving tens of billions of personalized recommendations a day under a latency of 30 milliseconds is a challenge. In this talk I'll share our algorithmic architecture, including its Spark-based offline layer, and its Elasticsearch-based serving layer, that enable running complex models under difficult scale constrains and shorten the cycle between research and production.
Sonya Liberman leads the Personalization team @ Outbrain's Recommendations group, developing large-scale machine learning algorithms for Outbrain's content recommendations platform serving tens of billions real-time recommendations a day. She specializes in Information Retrieval, Machine Learning, and Computational Linguistics. Before joining Outbrain, she led the Research and Algorithms @ ConvertMedia (acquired by Taboola). She holds an MSc in Computer Science and a BSc in Computer Science and Computational Biology.
This invited talk was given at PyData Meetup, April 2019
https://www.meetup.com/PyData-Tel-Aviv/
A new algorithm for inferring user search goals with feedback sessions
This document proposes a new algorithm to infer user search goals from query logs. It clusters feedback sessions from click-through data to discover different goals for an ambiguous query. It generates pseudo-documents to represent sessions for clustering. The algorithm evaluates inferred goals using a new metric called Classified Average Precision. Experiments on a commercial search engine validate the method effectively discovers user search goals.
Find and be Found: Information Retrieval at LinkedIn
Shakti Sinha and Daniel Tunkelang discuss how LinkedIn's search functionality works. They explain that LinkedIn search is personalized based on a user's profile and network. Query understanding involves tagging queries to determine entity types like people, companies, or skills. Ranking is also personalized using machine learning models trained on search logs to determine relevance for a specific user's query. The system aims to provide both globally and personally relevant results, as about two-thirds of clicks come from out of a user's network.
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
This document summarizes an algorithmic digital attribution model implemented using Spark. It begins with an overview of digital attribution and how algorithmic models can determine attribution weights. It then discusses how the model was implemented using Spark, including data processing, model building, and attribution calculations. Key lessons learned are around memory management, iterative computation, and error handling when working with Spark.
Intro to Google Analytics and Google AdWords (March 19 2013)
as part of the Chester County Marketing Group's monthly meetings, Jeff Tincher gave a presentation about Google AdWords and Analytics and how a small business could value from both.
Similar to Play with Kaggle (20)
Iterative Methodology for Personalization Models Optimization
Iterative Methodology for Personalization Models Optimization
moma-django overview --> Django + MongoDB: building a custom ORM layer
moma-django overview --> Django + MongoDB: building a custom ORM layer
Владимир Гулин, Mail.Ru Group, Learning to rank using clickthrough data
Владимир Гулин, Mail.Ru Group, Learning to rank using clickthrough data
Mozilla Foundation Metrics - presentation to engineers
Mozilla Foundation Metrics - presentation to engineers
Profiler for Smartphone Users Interests Using Modified Hierarchical Agglomera...
Profiler for Smartphone Users Interests Using Modified Hierarchical Agglomera...
A new algorithm for inferring user search goals with feedback sessions
A new algorithm for inferring user search goals with feedback sessions
A new algorithm for inferring user search goals with feedback sessions
A new algorithm for inferring user search goals with feedback sessions
Find and be Found: Information Retrieval at LinkedIn
Find and be Found: Information Retrieval at LinkedIn
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
Intro to Google Analytics and Google AdWords (March 19 2013)
Intro to Google Analytics and Google AdWords (March 19 2013)
Recently uploaded
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Recently uploaded (20)
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Play with Kaggle
- 2. Kaggle? Kaggle is a platform for predictive modeling competitions. “We're making data science into a sport.”
- 3. Let's enter a challenge!
- 4. The Data Noteworthy characteristics of the dataset: ● Unique queries: 21,073,569 ● Unique urls: 703,484,26 ● Unique users: 5,736,333 ● Training sessions: 34,573,630 ● Test sessions: 797,867 ● Clicks in the training data: 64,693,054 Total records in the log: 167,413,039 (=15Go!)
- 5. Let's shake the data
- 6. Logs format. Session 0 Session 1 Session 2 Session 3 Session 4
- 7. Sessions format. session 5 day 15 user 1 Term IDs (Url,Domain) ranked Session metadata Url clicked time passed
- 8. Evaluation. The URLs are labeled using 3 grades of relevance: {0, 1, 2} The labeling is done automatically, based on dwell-time. 0 : irrelevant - no clicks and clicks with dwell time < 50 time units. 1 : relevant - clicks with dwell time > 50 and < 399 time units. 2 : highly relevant - clicks with dwell time > 400 time units.
- 9. How to beat Yandex? Clicks count Rank So we have to sort better than that!
- 10. Step 1 : Reshape it! For each user we would estimate his click probability on an url
- 11. Step 2 : Cross validate Split your dataset like Yandex did: - On the last 3 days. - Only one session by user. Goal: auto-evaluate our model.
- 12. Step 3 : Add new features We add some informations on each user: - Did he see this url in the past? - Did he click on it? - How many times? - Did he skip it? - Had he ever click on a rank 9 url in the past?
- 13. Step 3 : Add new features The thing is, we don't want to re-rank all... So we add click entropy: For each query: Where p(x) is the percentage of clicks on document x among all clicks. Example: Small click entropy query: yahoo, youtube. Large click entropy query: photos, jobs.
- 14. Step 4 : the model. Goal: Predict the probability of click of an user on an url. Our training set: session url features... We use logistic regression and random forest. target
- 15. The leaderboard.
- 16. Thanks ! If you want to enter with us in a future challenge: contact@dataiku.com