Material presented at Tokyo Web Mining Meetup, March 26, 2016.
The source code is here:
https://github.com/hamukazu/tokyo.webmining.2016-03-26
東京ウェブマイニング(2016年3月27)の発表資料です。すべて英語です。
Data.Monks SGTM is a universal endpoint.pptxDougHall64
The sunset of GA360 can be the sunrise for your data collection.
This is a new approach involving a creative use of Server Side GTM (SGTM) and the only limit is your imagination.
Let's reimagine data collection by decoupling it from functionality and content. We'll explore this architecture through 5 example use cases that you might not have considered possible.
There's a fair stack of takeaway material including the actual solution source.
Product Launch Roadmap Quarterly Timeline Covering Milestone Marketing And SalesSlideTeam
Presenting this set of slides with name - Product Launch Roadmap Quarterly Timeline Covering Milestone Marketing And Sales. This is a four stage process. The stages in this process are Product Launch Roadmap, Product Launch Timeline, Product Launch Linear Process. https://bit.ly/358T3EK
Presentation at the Netflix Expo session at RecSys 2020 virtual conference on 2020-09-24. It provides an overview of recommendation and personalization at Netflix and then highlights some of the things we’ve been working on as well as some important open research questions in the field of recommendations.
Active Learning in Collaborative Filtering Recommender Systems : a SurveyUniversity of Bergen
In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.
This complete presentation has PPT slides on wide range of topics highlighting the core areas of your business needs. It has professionally designed templates with relevant visuals and subject driven content. This presentation deck has total of twenty four slides. Get access to the customizable templates. Our designers have created editable templates for your convenience. You can edit the colour, text and font size as per your need. You can add or delete the content if required. You are just a click to away to have this ready-made presentation. Click the download button now.
Challenges And Solution Powerpoint GraphicsSlideTeam
“You can download this product from SlideTeam.net”
Overcome your business challenges by using our challenges and solution PPT template. A three staged process with colored icons has been used to craft this business challenges and solutions PPT visual. This business challenges and solution presentation design is created in a way that can completely change your audience’s perspective and inspire them to connect to your presentation. Apart from this, the combination of this solutions to challenges PowerPoint diagram with icons can create various fruitful benefits to not only your organization but also to your investors, clients and the customers. The business solutions PowerPoint slideshow explains the importance of business solutions to key challenges which occur while achieving your goals. Use this solutions PPT slide for business and management related presentations and get good remarks. You can also add more content and icons to this if the need arises as the PPT shape if fully modifiable. There is a wide range of more business solutions and challenges PPT graphics which can display your content in a nice way and help you achieve your objectives. So simply start working with this PPT presentation diagram. Add to the apples in your basket. Our Challenges And Solution Powerpoint Graphics will build up your assets. https://bit.ly/3ogkw1U
Data.Monks SGTM is a universal endpoint.pptxDougHall64
The sunset of GA360 can be the sunrise for your data collection.
This is a new approach involving a creative use of Server Side GTM (SGTM) and the only limit is your imagination.
Let's reimagine data collection by decoupling it from functionality and content. We'll explore this architecture through 5 example use cases that you might not have considered possible.
There's a fair stack of takeaway material including the actual solution source.
Product Launch Roadmap Quarterly Timeline Covering Milestone Marketing And SalesSlideTeam
Presenting this set of slides with name - Product Launch Roadmap Quarterly Timeline Covering Milestone Marketing And Sales. This is a four stage process. The stages in this process are Product Launch Roadmap, Product Launch Timeline, Product Launch Linear Process. https://bit.ly/358T3EK
Presentation at the Netflix Expo session at RecSys 2020 virtual conference on 2020-09-24. It provides an overview of recommendation and personalization at Netflix and then highlights some of the things we’ve been working on as well as some important open research questions in the field of recommendations.
Active Learning in Collaborative Filtering Recommender Systems : a SurveyUniversity of Bergen
In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.
This complete presentation has PPT slides on wide range of topics highlighting the core areas of your business needs. It has professionally designed templates with relevant visuals and subject driven content. This presentation deck has total of twenty four slides. Get access to the customizable templates. Our designers have created editable templates for your convenience. You can edit the colour, text and font size as per your need. You can add or delete the content if required. You are just a click to away to have this ready-made presentation. Click the download button now.
Challenges And Solution Powerpoint GraphicsSlideTeam
“You can download this product from SlideTeam.net”
Overcome your business challenges by using our challenges and solution PPT template. A three staged process with colored icons has been used to craft this business challenges and solutions PPT visual. This business challenges and solution presentation design is created in a way that can completely change your audience’s perspective and inspire them to connect to your presentation. Apart from this, the combination of this solutions to challenges PowerPoint diagram with icons can create various fruitful benefits to not only your organization but also to your investors, clients and the customers. The business solutions PowerPoint slideshow explains the importance of business solutions to key challenges which occur while achieving your goals. Use this solutions PPT slide for business and management related presentations and get good remarks. You can also add more content and icons to this if the need arises as the PPT shape if fully modifiable. There is a wide range of more business solutions and challenges PPT graphics which can display your content in a nice way and help you achieve your objectives. So simply start working with this PPT presentation diagram. Add to the apples in your basket. Our Challenges And Solution Powerpoint Graphics will build up your assets. https://bit.ly/3ogkw1U
Example Presentation About Myself Interview PPT PowerPoint Presentation SlidesSlideTeam
Example Presentation About Myself Interview Ppt PowerPoint Presentation Slides are designed to showcase detailed work experience that helps you to leave a lasting impression on your viewers. This exclusive introduce yourself deck includes content ready slides such as the path to a career, SWOT analysis, personal qualifications, achievements, training, experience, case study, language skills, and hobbies, etc. It has templates with professional background images and relevant content. Introduce yourself PowerPoint template is perfect to structure an interview presentation. Using these PPT Visuals, you can make an organized format of your qualifications and experiences. The self-introduction PPT slides assist users to showcase their skills and abilities. These pre-designed introduce yourself PPT slides contains infographics that help to summarize individuals background on education, personal information, and professional experiences. Win the attention of your audience with our Job Interview Presentation on Yourself PowerPoint Presentation Slides.
When marketing teams spend money on a paid acquisitions program it is crucial to understand the effect of that ad spend. In this talk, we will outline incrementality as a way to measure the causal impact that ad spend has on acquiring new customers and its advantages over more traditional metrics. We will walk through several ad measurement products available today and give examples of how to apply them to your business.
Frequently Bought Together Recommendations Based on EmbeddingsDatabricks
We are the recommendation team that performs Data Engineering + Machine Learning + Software Engineering practices in “hepsiburada.com” which is the largest e-commerce platform in Turkey and in the Middle East. Our aim is to generate relevant recommendations to our users in the most appropriate manner in terms of time, context and products.
Every organization needs to adapt to the ever-changing business environment. Sensing this need, we have come up with these content-ready change management PowerPoint presentation slides. These change management PPT templates will help you deal with any kind of an organizational change. Be it with people, goals or processes. The business solutions incorporated here will help you identify the organizational structure, create vision for change, implement strategies, identify resistance and risk, manage cost of change, get feedback and evaluation, and much more. With the help of various change management tools and techniques illustrated in this presentation design, you can achieve the desired business outcomes. This business transition PowerPoint design also covers certain related topics such as change model, transformation strategy, change readiness, change control, project management and business process. By implementing the change control methods mentioned in the presentation, you will be able to have a smooth transition in an organization. So, without waiting much, download our extensively researched change management framework presentation. With our Change Management Presentation slides, understand the need for change and plan to go through it without any hassles.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Deep neural networks for Youtube recommendationsAryan Khandal
Deep Neural Networks for YouTube Recommendations by Paul Covington, Jay Adams, Emre Sargin. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy:first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user facing impact.
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
A/B Testing at Pinterest: Building a Culture of Experimentation WrangleConf
Presenter: Andrea Burbank, Pinterest
A successful experimentation program consists of much more than mere randomization and measurement. How do you help stakeholders understand the right things to measure, avoid common pitfalls, and learn to rely on A/B tests as the best way to measure a new system or feature? In this talk, Andrea will explain how building a culture of experimentation and the right tools to support it is just as important as the statistics behind the comparisons themselves - and potentially much trickier to get right.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on Graph Neural Networks for Recommendations.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/4aXk3LNTJRc
Business Framework Value Proposition PowerPoint Presentation SlidesSlideTeam
Are you preparing to give PowerPoint presentation on business framework value proposition? Not finding the right designs and graphics? No worries! We present you, our predesigned and content ready business framework value proposition PowerPoint presentation templates. This customer value proposition PPT presentation will be useful for middle-level management to define company’s internal strategy to the senior level. It includes company overview, product, and services, elevator pitch, problem areas, find a solution, value proposition product & services, company revenue model & expense model. It also includes company’s growth strategy, competitive landscape, two product comparison, SWOT analysis, business shareholders pattern etc. Want to make PPT slides on business strategy, value proposition, personal value proposition, company values, customer value proposition framework, value proposition model and value marketing, employee value proposition, strategic management, value proposition model. You can deploy this value-focused enterprise model presentation. Download business framework value proposition PowerPoint presentation slides now. Get everyone to come to an agreement with our Business Framework Value Proposition PowerPoint Presentation Slides. It helps identify disputed aspects.
What’s next for deep learning for Search?Bhaskar Mitra
In this talk, I will share some of my personal reflections on the progress in the field of neural IR and some of the ongoing and future research directions that I am personally excited about. This talk will be informed by my own research in this area as well as my experience both as a developer/organizer of the MS MARCO benchmark and the TREC Deep Learning Track and as an applied researcher previously working on web scale search systems at Bing. My goal in this talk would be to move the conversation beyond neural reranking models towards a richer and bolder vision of search powered by deep learning.
MeasureCamp 2022: Digital Analytics Solutions for 2022Lukáš Čech
Approaches to select the right digital analytics solution that will fulfill the business needs and fit into the existing ecosystem and company culture.
An opportunity for the savvy marketer to connect early and make a deeper, more meaningful relationship with prospects – through lead nurturing.
1. Build custom buyer personas
2. Make all your marketing touchpoints personal
3. Use social marketing effectively
4. Provide the right content at the right point in the cycle
5. and much, much more!
Recommendation System --Theory and PracticeKimikazu Kato
Survey on recommendation systems presented at IMI Colloquium, Kyushu University, Feb 18, 2015.
レコメンデーションシステムの最新の研究動向に関する解説です。2015年2月18日に九州大学IMIコロキアムで講演したものです。資料は英語ですが、講演は日本語でやりました。
Example Presentation About Myself Interview PPT PowerPoint Presentation SlidesSlideTeam
Example Presentation About Myself Interview Ppt PowerPoint Presentation Slides are designed to showcase detailed work experience that helps you to leave a lasting impression on your viewers. This exclusive introduce yourself deck includes content ready slides such as the path to a career, SWOT analysis, personal qualifications, achievements, training, experience, case study, language skills, and hobbies, etc. It has templates with professional background images and relevant content. Introduce yourself PowerPoint template is perfect to structure an interview presentation. Using these PPT Visuals, you can make an organized format of your qualifications and experiences. The self-introduction PPT slides assist users to showcase their skills and abilities. These pre-designed introduce yourself PPT slides contains infographics that help to summarize individuals background on education, personal information, and professional experiences. Win the attention of your audience with our Job Interview Presentation on Yourself PowerPoint Presentation Slides.
When marketing teams spend money on a paid acquisitions program it is crucial to understand the effect of that ad spend. In this talk, we will outline incrementality as a way to measure the causal impact that ad spend has on acquiring new customers and its advantages over more traditional metrics. We will walk through several ad measurement products available today and give examples of how to apply them to your business.
Frequently Bought Together Recommendations Based on EmbeddingsDatabricks
We are the recommendation team that performs Data Engineering + Machine Learning + Software Engineering practices in “hepsiburada.com” which is the largest e-commerce platform in Turkey and in the Middle East. Our aim is to generate relevant recommendations to our users in the most appropriate manner in terms of time, context and products.
Every organization needs to adapt to the ever-changing business environment. Sensing this need, we have come up with these content-ready change management PowerPoint presentation slides. These change management PPT templates will help you deal with any kind of an organizational change. Be it with people, goals or processes. The business solutions incorporated here will help you identify the organizational structure, create vision for change, implement strategies, identify resistance and risk, manage cost of change, get feedback and evaluation, and much more. With the help of various change management tools and techniques illustrated in this presentation design, you can achieve the desired business outcomes. This business transition PowerPoint design also covers certain related topics such as change model, transformation strategy, change readiness, change control, project management and business process. By implementing the change control methods mentioned in the presentation, you will be able to have a smooth transition in an organization. So, without waiting much, download our extensively researched change management framework presentation. With our Change Management Presentation slides, understand the need for change and plan to go through it without any hassles.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Deep neural networks for Youtube recommendationsAryan Khandal
Deep Neural Networks for YouTube Recommendations by Paul Covington, Jay Adams, Emre Sargin. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy:first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user facing impact.
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
A/B Testing at Pinterest: Building a Culture of Experimentation WrangleConf
Presenter: Andrea Burbank, Pinterest
A successful experimentation program consists of much more than mere randomization and measurement. How do you help stakeholders understand the right things to measure, avoid common pitfalls, and learn to rely on A/B tests as the best way to measure a new system or feature? In this talk, Andrea will explain how building a culture of experimentation and the right tools to support it is just as important as the statistics behind the comparisons themselves - and potentially much trickier to get right.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on Graph Neural Networks for Recommendations.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/4aXk3LNTJRc
Business Framework Value Proposition PowerPoint Presentation SlidesSlideTeam
Are you preparing to give PowerPoint presentation on business framework value proposition? Not finding the right designs and graphics? No worries! We present you, our predesigned and content ready business framework value proposition PowerPoint presentation templates. This customer value proposition PPT presentation will be useful for middle-level management to define company’s internal strategy to the senior level. It includes company overview, product, and services, elevator pitch, problem areas, find a solution, value proposition product & services, company revenue model & expense model. It also includes company’s growth strategy, competitive landscape, two product comparison, SWOT analysis, business shareholders pattern etc. Want to make PPT slides on business strategy, value proposition, personal value proposition, company values, customer value proposition framework, value proposition model and value marketing, employee value proposition, strategic management, value proposition model. You can deploy this value-focused enterprise model presentation. Download business framework value proposition PowerPoint presentation slides now. Get everyone to come to an agreement with our Business Framework Value Proposition PowerPoint Presentation Slides. It helps identify disputed aspects.
What’s next for deep learning for Search?Bhaskar Mitra
In this talk, I will share some of my personal reflections on the progress in the field of neural IR and some of the ongoing and future research directions that I am personally excited about. This talk will be informed by my own research in this area as well as my experience both as a developer/organizer of the MS MARCO benchmark and the TREC Deep Learning Track and as an applied researcher previously working on web scale search systems at Bing. My goal in this talk would be to move the conversation beyond neural reranking models towards a richer and bolder vision of search powered by deep learning.
MeasureCamp 2022: Digital Analytics Solutions for 2022Lukáš Čech
Approaches to select the right digital analytics solution that will fulfill the business needs and fit into the existing ecosystem and company culture.
An opportunity for the savvy marketer to connect early and make a deeper, more meaningful relationship with prospects – through lead nurturing.
1. Build custom buyer personas
2. Make all your marketing touchpoints personal
3. Use social marketing effectively
4. Provide the right content at the right point in the cycle
5. and much, much more!
Recommendation System --Theory and PracticeKimikazu Kato
Survey on recommendation systems presented at IMI Colloquium, Kyushu University, Feb 18, 2015.
レコメンデーションシステムの最新の研究動向に関する解説です。2015年2月18日に九州大学IMIコロキアムで講演したものです。資料は英語ですが、講演は日本語でやりました。
Facebook Talk at Netflix ML Platform meetup Sep 2019Faisal Siddiqi
In this talk at the Netflix Machine Learning Platform Meetup on 12 Sep 2019, Sam Daulton from Facebook discusses "Practical Solutions to real-world exploration problems".
Determination of Optimal Product Mix for Profit Maximization using Linear Pro...IJERA Editor
This paper demonstrates the use of liner programming methods in order to determine the optimal product mix for
profit maximization. There had been several papers written to demonstrate the use of linear programming in
finding the optimal product mix in various organization. This paper is aimed to show the generic approach to be
taken to find the optimal product mix.
Determination of Optimal Product Mix for Profit Maximization using Linear Pro...IJERA Editor
This paper demonstrates the use of liner programming methods in order to determine the optimal product mix for
profit maximization. There had been several papers written to demonstrate the use of linear programming in
finding the optimal product mix in various organization. This paper is aimed to show the generic approach to be
taken to find the optimal product mix.
Linear, Machine Learning or Probabilistic Predictive Models: What's Best for ...Bohdan Pavlyshenko
Linear, Machine Learning and Probabilistic models are often used in the predictive analytics. Each of them has its pros and cons for different industrial and business problems. Linear models make it possible to extrapolate forecasting, study impact of external factors but does not allow us to capture nonlinear complicated patterns in the data. Machine learning models can find a complicated pattern but only in the stationary data, at the same time these models require a lot of historical data for training to get sufficient accuracy. Probabilistic models based on the Bayesian inference can take into account expert opinion via prior distributions for parameters and can be used for different kinds of risk assessments. In the speech, I am going to consider the use of these models and their combinations in different use cases. One type of use case is numeric regression for time series forecasting, another one is logistic regression in manufacturing failure detection problems. I will also consider multilevel predictive ensembles of models based on the bagging and stacking approaches.
Simulators play a major role in analyzing multi-modal transportation networks. As their complexity increases, optimization becomes an increasingly challenging task. Current calibration procedures often rely on heuristics, rules of thumb and sometimes on brute-force search. Alternatively, we provide a statistical method which combines a distributed, Gaussian Process Bayesian optimization method with dimensionality reduction techniques and structural improvement. We then demonstrate our framework on the problem of calibrating a multi-modal transportation network of city of Bloomington, Illinois. Our framework is sample efficient and supported by theoretical analysis and an empirical study. We demonstrate on the problem of calibrating a multi-modal transportation network of city of Bloomington, Illinois. Finally, we discuss directions for further research.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
Presented at PyCon JP 2014.
Video is available at
http://bit.ly/1tXYhw6
This talk explores case studies of effective usage of Numpy/Scipy and shows that the computational speed sometimes improves drastically with the appropriate derivation of formulas and performance-conscious implementation. I especially focus on scipy.sparse, the module for sparse matrices, which is often useful in the areas of machine learning and natural language processing.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
Introduction to behavior based recommendation system
1. Introduction to Algorithms for Behavior Based
Recommendation
Tokyo Web Mining Meetup
March 26, 2016
Kimikazu Kato
Silver Egg Technology Co., Ltd.
1 / 36
2. About myself
加藤公一 Kimikazu Kato
Twitter: @hamukazu
LinkedIn: http://linkedin.com/in/kimikazukato
Chief Scientist at Silver Egg Technology
Ph.D in computer science, Master's degree in mathematics
Experience in numerical computation and mathematical algorithms
especially ...
Geometric computation, computer graphics
Partial differential equation, parallel computation, GPGPU
Mathematical programming
Now specialize in
Machine learning, especially, recommendation system
2 / 36
3. About our company
Silver Egg Technology
Established: 1998
CEO: Tom Foley
Main Service: Recommendation System, Online Advertisement
Major Clients: QVC, Senshukai (Bellemaison), Tsutaya
We provide a recommendation system to Japan's leading web sites.
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6. Recommendation System
Recommender systems or recommendation systems (sometimes
replacing "system" with a synonym such as platform or engine) are a
subclass of information filtering system that seek to predict the
'rating' or 'preference' that user would give to an item. — Wikipedia
In this talk, we focus on collaborative filtering method, which only utilize
users' behavior, activity, and preference.
Other methods include:
Content-based methods
Method using demographic data
Hybrid
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7. Rating Prediction Problem
usermovie W X Y Z
A 5 4 1 4
B 4
C 2 3
D 1 4 ?
Given rating information for some user/movie pairs,
Want to predict a rating for an unknown user/movie pair.
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8. Item Prediction Problem
useritem W X Y Z
A 1 1 1 1
B 1
C 1
D 1 ? 1 ?
Given "who bought what" information (user/item pairs),
Want to predict which item is likely to be bought by a user.
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9. Input/Output of the systems
Rating Prediction
Input: set of ratings for user/item pairs
Output: map from user/item pair to predicted rating
Item Prediction
Input: set of user/item pairs as shopping data, integer
Output: top items for each user which are most likely to be bought by
him/her
k
k
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10. Evaluation Metrics for Recommendation
Systems
Rating prediction
The Root of the Mean Squared Error (RMSE)
The square root of the sum of squared errors
Item prediction
Precision
(# of Recommended and Purchased)/(# of Recommended)
Recall
(# of Recommended and Purchased)/(# of Purchased)
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11. RMSE of Rating Prediction
Some user/item pairs are randomly chosen to be hidden.
usermovie W X Y Z
A 5 4 1 4
B 4
C 2 3
D 1 4 ?
Predicted as 3.1 but the actual is 4, then the squared error is
.
Take the sum over the error over all the hidden items and then, take the
square root of it.
|3.1 − 4 =|
2
0.9
2
( −∑
(u,i)∈hidden
predictedui
actualui )
2
− −−−−−−−−−−−−−−−−−−−−−−−−−
√
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12. Precision/Recall of Item Prediction
If three items are recommended:
2 out of 3 recommended items are actually bought: the precision is 2/3.
2 out of 4 bought items are recommended: the recall is 2/4.
These are denoted by recall@3 and prec@3.
Ex. recall@5 = 3/5, prec@5 = 3/4
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13. ROC and AUC
# of
recom.
1 2 3 4 5 6 7 8 9 10
# of
whites
1 1 1 2 2 3 4 5 5 6
# of
blacks
0 1 2 2 3 3 3 3 4 4
Divide the first and second row by total number of white and blacks
respectively, and plot the values in xy plane.
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14. This curve is called "ROC curve." The area under this curve is called "AUC."
Higher AUC is better (max =1).
The AUC is often used in academia, but for a practical purpose...
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15. Netflix Prize
The Netflix Prize was an open competition for the best collaborative
filtering algorithm to predict user ratings for films, based on previous
ratings without any other information about the users or films, i.e.
without the users or the films being identified except by numbers
assigned for the contest. — Wikipedia
Shortly, an open competition for preference prediction.
Closed in 2009.
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16. Outline of Winner's Algorithm
Refer to the blog by E.Chen.
http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
Digest of the methods:
Neighborhood Method
Matrix Factorization
Restricted Boltzmann Machines
Regression
Regularization
Ensemble Methods
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17. Notations
Number of users:
Set of users:
Number of items (movies):
Set of items (movies):
Input matrix: ( matrix)
n
U = {1, 2, … , n}
m
I = {1, 2, … , m}
A n × m
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18. Matrix Factorization
Based on the assumption that each item is described by a small number of
latent factors
Each rating is expressed as a linear combination of the latent factors
Achieve good performance in Netflix Prize
Find such matrices , where
A ≈ YX
T
X ∈ Mat(f, n) Y ∈ Mat(f, m) f ≪ n, m
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19. Find and maximize
p (A|X, Y , σ) = N ( | , σ)∏
≠0aui
Aui X
T
u Yi
p(X| ) = N ( |0, I)σX ∏
u
Xu σX
p(Y | ) = N ( |0, I)σY ∏
i
Yi σY
X Y p (X, Y |A, σ)
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20. According to Bayes' Theorem,
Thus,
where means Frobenius norm.
How can this be computed? Use MCMC. See [Salakhutdinov et al., 2008].
Once and are determined, and the prediction for is
estimated by
p (X, Y |A, σ)
= p(A|X, Y , σ)p(X| )p(X| ) × const.σX σX
log p (U , V |A, σ, , )σU σV
= + ∥X + ∥Y + const.∑
Aui
( − )Aui X
T
u Yi
2
λX ∥
2
Fro
λY ∥
2
Fro
∥ ⋅ ∥Fro
X Y := YA
~
X
T
Aui
A
~
ui
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21. Rating
usermovie W X Y Z
A 5 4 1 4
B 4
C 2 3
D 1 4 ?
Includes negative feedback
"1" means "boring"
Zero means "unknown"
Shopping (Browsing)
useritem W X Y Z
A 1 1 1 1
B 1
C 1
D 1 ? 1 ?
Includes no negative feedback
Zero means "unknown" or
"negative"
More degree of the freedom
Difference between Rating and Shopping
Consequently, the algorithm effective for the rating matrix is not necessarily
effective for the shopping matrix.
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23. Adding a Constraint
The problem has the too much degree of freedom
Desirable characteristic is that many elements of the product should be
zero.
Assume that a certain ratio of zero elements of the input matrix remains
zero after the optimization [Sindhwani et al., 2010]
Experimentally outperform the "zero-as-negative" method
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24. One-class Matrix Completion
[Sindhwani et al., 2010]
Introduced variables to relax the problem.
Minimize
subject to
pui
( − ) + ∥X + ∥Y∑
≠0Aui
Aui X
T
u Yi λX ∥
2
Fro
λY ∥
2
Fro
+ [ (0 − + (1 − )(1 − ]∑
=0Aui
pui X
T
u Yi )
2
pui X
T
u Yi )
2
+ T [− log − (1 − ) log(1 − )]∑
=0Aui
pui pui pui pui
= r
1
|{ | = 0}|Aui Aui
∑
=0Aui
pui
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25. Intuitive explanation:
means how likely the -element is zero.
The second term is the error of estimation considering 's.
The third term is the entropy of the distribution.
( − ) + ∥X + ∥Y∑
≠0Aui
Aui X
T
u Yi λX ∥
2
Fro
λY ∥
2
Fro
+ [ (0 − + (1 − )(1 − ]∑
=0Aui
pui X
T
u Yi )
2
pui X
T
u Yi )
2
+ T [− log − (1 − ) log(1 − )]∑
=0Aui
pui pui pui pui
pui (u, i)
pui
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26. Implicit Sparseness constraint: SLIM (Elastic Net)
In the regression model, adding L1 term makes the solution sparse:
The similar idea is used for the matrix factorization [Ning et al., 2011]:
Minimize
subject to
[ ∥Xw − y + ∥w + λρ|w ]min
w
1
2n
∥
2
2
λ(1 − ρ)
2
∥
2
2
|1
∥A − AW ∥ + ∥W + λρ|W
λ(1 − ρ)
2
∥
2
Fro
|1
diag W = 0
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27. Ranking prediction
Another strategy of shopping prediction
"Learn from the order" approach
Predict whether X is more likely to be bought than Y, rather than the
probability for X or Y.
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28. Bayesian Probabilistic Ranking
[Rendle et al., 2009]
Consider matrix factorization model, but the update of elements is
according to the observation of the "orders"
The parameters are the same as usual matrix factorization, but the
objective function is different
Consider a total order for each . Suppose that
means "the user is more likely to buy than .
The objective is to calculate such that and (which
means and are not bought by ).
>u u ∈ U i j(i, j ∈ I)>u
u i j
p(i j)>u = 0Aui Auj
i j u
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29. Let
and define
where we assume
According to Bayes' theorem, the function to be optimized becomes:
= {(u, i, j) ∈ U × I × I| = 1, = 0} ,DA Aui Auj
p( |X, Y ) := p(i j|X, Y )∏
u∈U
>u ∏
(u,i,j)∈DA
>u
p(i j|X, Y )>u
σ(x)
= σ( − )X
T
u Yi Xu Yj
=
1
1 + e
−x
∏ p(X, Y | ) = ∏ p( |X, Y ) × p(X)p(Y ) × const.>u >u
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30. Taking log of this,
Now consider the following problem:
This means "find a pair of matrices which preserve the order of the
element of the input matrix for each ."
L := log[∏ p( |X, Y ) × p(X)p(Y )]>u
= log p(i j|X, Y ) − ∥X − ∥Y∏
(u,i,j)∈DA
>u λX ∥
2
Fro
λY ∥
2
Fro
= log σ( − ) − ∥X − ∥Y∑
(u,i,j)∈DA
X
T
u Yi X
T
u Yj λX ∥
2
Fro
λY ∥
2
Fro
[ log σ( − ) − ∥X − ∥Y ]max
X,Y
∑
(u,i,j)∈DA
X
T
u Yi X
T
u Yj λX ∥
2
Fro
λY ∥
2
Fro
X, Y
u
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31. Computation
The function we want to optimize:
is huge, so in practice, a stochastic method is necessary.
Let the parameters be .
The algorithm is the following:
Repeat the following
Choose randomly
Update with
This method is called Stochastic Gradient Descent (SGD).
log σ( − ) − ∥X − ∥Y∑
(u,i,j)∈DA
X
T
u Yi X
T
u Yj λX ∥
2
Fro
λY ∥
2
Fro
U × I × I
Θ = (X, Y )
(u, i, j) ∈ DA
Θ
Θ = Θ − α (log σ( − ) − ∥X − ∥Y )
∂
∂Θ
X
T
u Yi X
T
u Yj λX ∥
2
Fro
λY ∥
2
Fro
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33. Practical Aspect of Recommendation
Problem
Computational time
Memory consumption
How many services can be integrated in a server rack?
Super high accuracy with a super computer is useless for real business
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34. Concluding Remarks: What is Important for
Good Prediction?
Theory
Machine learning
Mathematical optimization
Implementation
Algorithms
Computer architecture
Mathematics
Human factors!
Hand tuning of parameters
Domain specific knowledge
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35. References (1/2)
For beginers
比戸ら, データサイエンティスト養成読本 機械学習入門編, 技術評論社, 2016
T.Segaran. Programming Collective Intelligence, O'Reilly Media, 2007.
E.Chen. Winning the Netflix Prize: A Summary.
A.Gunawardana and G.Shani. A Survey of Accuracy Evaluation Metrics of
Recommendation Tasks, The Journal of Machine Learning Research,
Volume 10, 2009.
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36. References (2/2)
Papers
Salakhutdinov, Ruslan, and Andriy Mnih. "Bayesian probabilistic matrix
factorization using Markov chain Monte Carlo." Proceedings of the 25th
international conference on Machine learning. ACM, 2008.
Sindhwani, Vikas, et al. "One-class matrix completion with low-density
factorizations." Data Mining (ICDM), 2010 IEEE 10th International
Conference on. IEEE, 2010.
Rendle, Steffen, et al. "BPR: Bayesian personalized ranking from implicit
feedback." Proceedings of the Twenty-Fifth Conference on Uncertainty in
Artificial Intelligence. AUAI Press, 2009.
Zou, Hui, and Trevor Hastie. "Regularization and variable selection via the
elastic net." Journal of the Royal Statistical Society: Series B (Statistical
Methodology) 67.2 (2005): 301-320.
Ning, Xia, and George Karypis. "SLIM: Sparse linear methods for top-n
recommender systems." Data Mining (ICDM), 2011 IEEE 11th
International Conference on. IEEE, 2011.
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