Recommendation systems
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An introduction to Recommendation engines and how these systems work. Both content based and collaborative filtering models are introduced. Hotel recommendation system is explained as a case study.
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Recommender Systems In Industry
This document outlines key concepts in recommendation systems. It begins by defining the traditional recommender problem as predicting user ratings for items based on past behavior and relationships. It then discusses lessons learned from the Netflix Prize competition, including the effectiveness of singular value decomposition and the limitations of models designed only for rating prediction. The document outlines approaches beyond rating prediction, including ranking, similarity, social recommendations, and explore/exploit tradeoffs. It discusses optimizing recommendation pages and using higher-order models like tensor factorization. In summary, it provides an overview of traditional and modern approaches in recommendation systems.
Recommender system
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Recommender Systems
Part of the course "Algorithmic Methods of Data Science". Sapienza University of Rome, 2015.
http://aris.me/index.php/data-mining-ds-2015
Recommender Systems
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
An introduction to Recommender Systems
Recommender systems are software agents that analyze a user's preferences through transactions and provide personalized recommendations accordingly. There are several recommendation paradigms including non-personalized rules, personalized rules based on user data, and transaction-based collaborative filtering that learns from user interactions. Context-based recommender systems also consider additional information like time, location, or device to provide adaptive recommendations. Common techniques used in recommender systems include content-based filtering that recommends similar items, collaborative filtering that finds users with similar tastes, and demographic-based recommendations.
Recommender systems: Content-based and collaborative filtering
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
Recommender Systems
The document discusses recommender systems and describes several techniques used in collaborative filtering recommender systems including k-nearest neighbors (kNN), singular value decomposition (SVD), and similarity weights optimization (SWO). It provides examples of how these techniques work and compares kNN to SWO. The document aims to explain state-of-the-art recommender system methods.
Crafting Recommenders: the Shallow and the Deep of it!
Sudeep Das presented on recommender systems and advances in deep learning approaches. Matrix factorization is still the foundational method for collaborative filtering, but deep learning models are now augmenting these approaches. Deep neural networks can learn hierarchical representations of users and items from raw data like images, text, and sequences of user actions. Models like wide and deep networks combine the strengths of memorization and generalization. Sequence models like recurrent neural networks have also been applied to sessions for next item recommendation.
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Recommender Systems In Industry
This document outlines key concepts in recommendation systems. It begins by defining the traditional recommender problem as predicting user ratings for items based on past behavior and relationships. It then discusses lessons learned from the Netflix Prize competition, including the effectiveness of singular value decomposition and the limitations of models designed only for rating prediction. The document outlines approaches beyond rating prediction, including ranking, similarity, social recommendations, and explore/exploit tradeoffs. It discusses optimizing recommendation pages and using higher-order models like tensor factorization. In summary, it provides an overview of traditional and modern approaches in recommendation systems.
Recommender system
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Recommender Systems
Part of the course "Algorithmic Methods of Data Science". Sapienza University of Rome, 2015.
http://aris.me/index.php/data-mining-ds-2015
Recommender Systems
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
An introduction to Recommender Systems
Recommender systems are software agents that analyze a user's preferences through transactions and provide personalized recommendations accordingly. There are several recommendation paradigms including non-personalized rules, personalized rules based on user data, and transaction-based collaborative filtering that learns from user interactions. Context-based recommender systems also consider additional information like time, location, or device to provide adaptive recommendations. Common techniques used in recommender systems include content-based filtering that recommends similar items, collaborative filtering that finds users with similar tastes, and demographic-based recommendations.
Recommender systems: Content-based and collaborative filtering
This document provides an overview of recommender systems, including content-based and collaborative filtering approaches. It discusses how content-based systems make recommendations based on item profiles and calculating similarity between user and item profiles. Collaborative filtering is described as finding similar users and making predictions based on their ratings. The document also covers evaluation metrics, complexity issues, and tips for building recommender systems.
Recommender Systems
The document discusses recommender systems and describes several techniques used in collaborative filtering recommender systems including k-nearest neighbors (kNN), singular value decomposition (SVD), and similarity weights optimization (SWO). It provides examples of how these techniques work and compares kNN to SWO. The document aims to explain state-of-the-art recommender system methods.
Crafting Recommenders: the Shallow and the Deep of it!
Sudeep Das presented on recommender systems and advances in deep learning approaches. Matrix factorization is still the foundational method for collaborative filtering, but deep learning models are now augmenting these approaches. Deep neural networks can learn hierarchical representations of users and items from raw data like images, text, and sequences of user actions. Models like wide and deep networks combine the strengths of memorization and generalization. Sequence models like recurrent neural networks have also been applied to sessions for next item recommendation.
Introduction to Recommendation Systems
1) Recommendation Systems in Practice
2) Types of Recommendation Systems
3) Building Data Pipeline for Video Recommendation System
Deep Learning for Recommender Systems
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Recommender systems using collaborative filtering
This document summarizes a student project on implementing recommender systems. The project objectives were to design a website using user-based, item-based, and model-based collaborative filtering as well as MapReduce to generate movie recommendations. The system was tested on the MovieLens dataset using MAE and RMSE metrics, with user-based filtering found to have the best performance. The document outlines the technical aspects of the recommendation system including the technologies used, website architecture, and references.
Recommender system introduction
This document discusses recommender systems, including:
1. It provides an overview of recommender systems, their history, and common problems like top-N recommendation and rating prediction.
2. It then discusses what makes a good recommender system, including experiment methods like offline, user surveys, and online experiments, as well as evaluation metrics like prediction accuracy, diversity, novelty, and user satisfaction.
3. Key metrics that are important to evaluate recommender systems are discussed, such as user satisfaction, prediction accuracy, coverage, diversity, novelty, serendipity, trust, robustness, and response time. The document emphasizes selecting metrics based on business goals.
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Tutorial Deepak Agarwal and me gave at the 2016 ACM Recommender Systems conference. These slides only cover my part.
Recommendation Systems
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
Overview of recommender system
The document provides an overview of recommender systems. It discusses the typical architecture of recommender systems and describes three main types: collaborative filtering systems, content-based systems, and knowledge-based systems. It also covers paradigms like collaborative filtering, content-based, knowledge-based, and hybrid recommender systems. The document then focuses on collaborative filtering techniques like user-based nearest neighbor collaborative filtering and item-based collaborative filtering. It also discusses latent factor models, matrix factorization approaches, and context-based recommender systems.
Recommendation engines
This document provides an overview of recommendation engines and systems. It describes different types of recommendation approaches, including collaborative filtering, content-based filtering, and hybrid methods. It also discusses how recommendation algorithms work and are implemented in Apache Mahout, a machine learning library for developing scalable recommendation applications. Key recommendation techniques like item-based filtering and user-based filtering are explained.
A Hybrid Recommendation system
The document introduces different types of recommender systems including search-based recommendations, category-based recommendations, collaborative filtering, clustering, association rules, and information filtering. It discusses the key aspects of each approach such as how recommendations are generated, advantages, and limitations. The document also presents a taxonomy for classifying recommender systems based on factors like targeted customer inputs, community inputs, recommendation methods, and outputs.
Recommendation system
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
Incorporating Diversity in a Learning to Rank Recommender System
Diversity is a desirable property of recommendations. Diversity can be increased with the use of re-rankers. This work presents an alternative approach where diversity is optimised together with accuracy during a matrix factorisation learning.
Recommender systems for E-commerce
This document discusses building a recommendation system for e-commerce. It begins by noting the importance of recommendations, with over 30% of online purchases coming from recommendations. It then discusses gathering data, both explicitly via ratings and reviews, and implicitly via user actions. Main approaches covered include content-based filtering, collaborative filtering using user-user and item-item similarities, and matrix factorization. The document also addresses challenges like sparsity, cold starts, scalability and privacy considerations in implementing recommendation systems.
Deep Learning for Recommender Systems - Budapest RecSys Meetup
1. Deep learning techniques such as convolutional neural networks, recurrent neural networks, and autoencoders can be applied to recommender systems.
2. Convolutional neural networks are commonly used to extract features from images, audio, and video that can then be used for recommendation. Recurrent neural networks can model user sessions as sequences of clicks.
3. Autoencoders learn lower-dimensional representations of items that capture similarities and can be used to make recommendations, especially for cold start problems where little is known about new users or items.
Recommendation system
The document discusses recommendation systems and machine learning models for recommendations. It covers the goals of recommendation systems, basic models including collaborative filtering, content-based, and knowledge-based systems. Neighborhood-based collaborative filtering is explained along with matrix factorization models. Deep learning methods for recommendations are also summarized, including neural collaborative filtering, graph-based models, and temporal models that handle dynamic graphs.
Deep Learning for Recommender Systems
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
Machine Learning for Recommender Systems MLSS 2015 Sydney
The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.
Calibrated Recommendations
This document proposes a calibrated recommendations approach that aims to provide recommendations that reflect all of a user's interests in correct proportions. Standard recommender systems trained for accuracy can lead to unbalanced recommendations that amplify a user's main interests and crowd out lesser interests. The calibrated recommendations approach uses a post-processing re-ranking step to optimize a submodular calibration metric, balancing accuracy and fairness by recommending items from all a user's interests in their correct proportions. Experiments on MovieLens data show that calibration can be improved significantly without degrading accuracy much.
Tutorial: Context In Recommender Systems
This document provides an overview of a tutorial on context-aware recommender systems. The tutorial will cover traditional recommendation techniques, context-aware recommendation which incorporates additional contextual information such as time and location, and context suggestion. It includes an agenda with topics, background information on recommender systems and evaluation metrics, and descriptions of techniques for context-aware recommendation including context filtering and modeling.
Boston ML - Architecting Recommender Systems
This document provides an overview of key concepts in recommender systems, including:
- The components of a recommender system including users, items, interactions, features, representations, predictions, loss functions, and learning.
- Design considerations for recommender systems such as choosing appropriate interaction values, features, representation functions, prediction functions, and loss functions.
- Examples of different types of recommender systems including collaborative filtering, content-based, hybrid, and real-world systems from Netflix, YouTube, and e-commerce.
- Tools for building recommender systems in Python like Implicit, Scikit-Learn, LightFM, TensorRec, and Annoy.
The document discusses the architecture
Content based filtering
This document discusses content-based recommendation techniques. It explains that content-based recommendation systems learn a user's preferences based on item attributes and characteristics to recommend similar items. It describes representing items and user profiles as vectors of keywords and computing similarity using metrics like cosine similarity. Finally, it briefly outlines probabilistic recommendation methods and linear classifiers for recommendations.
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20120140506003
This document summarizes and discusses recommender systems. It begins by defining recommender systems and their purpose of presenting personalized recommendations of items likely to interest users based on their profiles and preferences. It then outlines three main recommendation techniques: content-based filtering which uses item attributes to make recommendations; collaborative filtering which identifies similar users to make recommendations; and hybrid filtering which combines the two approaches. Finally, it discusses challenges for non-personalized recommendation systems in serving diverse user groups and notes that personalized approaches may help address this.
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Introduction to Recommendation Systems
1) Recommendation Systems in Practice
2) Types of Recommendation Systems
3) Building Data Pipeline for Video Recommendation System
Deep Learning for Recommender Systems
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Recommender systems using collaborative filtering
This document summarizes a student project on implementing recommender systems. The project objectives were to design a website using user-based, item-based, and model-based collaborative filtering as well as MapReduce to generate movie recommendations. The system was tested on the MovieLens dataset using MAE and RMSE metrics, with user-based filtering found to have the best performance. The document outlines the technical aspects of the recommendation system including the technologies used, website architecture, and references.
Recommender system introduction
This document discusses recommender systems, including:
1. It provides an overview of recommender systems, their history, and common problems like top-N recommendation and rating prediction.
2. It then discusses what makes a good recommender system, including experiment methods like offline, user surveys, and online experiments, as well as evaluation metrics like prediction accuracy, diversity, novelty, and user satisfaction.
3. Key metrics that are important to evaluate recommender systems are discussed, such as user satisfaction, prediction accuracy, coverage, diversity, novelty, serendipity, trust, robustness, and response time. The document emphasizes selecting metrics based on business goals.
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Tutorial Deepak Agarwal and me gave at the 2016 ACM Recommender Systems conference. These slides only cover my part.
Recommendation Systems
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
Overview of recommender system
The document provides an overview of recommender systems. It discusses the typical architecture of recommender systems and describes three main types: collaborative filtering systems, content-based systems, and knowledge-based systems. It also covers paradigms like collaborative filtering, content-based, knowledge-based, and hybrid recommender systems. The document then focuses on collaborative filtering techniques like user-based nearest neighbor collaborative filtering and item-based collaborative filtering. It also discusses latent factor models, matrix factorization approaches, and context-based recommender systems.
Recommendation engines
This document provides an overview of recommendation engines and systems. It describes different types of recommendation approaches, including collaborative filtering, content-based filtering, and hybrid methods. It also discusses how recommendation algorithms work and are implemented in Apache Mahout, a machine learning library for developing scalable recommendation applications. Key recommendation techniques like item-based filtering and user-based filtering are explained.
A Hybrid Recommendation system
The document introduces different types of recommender systems including search-based recommendations, category-based recommendations, collaborative filtering, clustering, association rules, and information filtering. It discusses the key aspects of each approach such as how recommendations are generated, advantages, and limitations. The document also presents a taxonomy for classifying recommender systems based on factors like targeted customer inputs, community inputs, recommendation methods, and outputs.
Recommendation system
Recommendation systems provide users with information they may be interested in based on their preferences and interests. They help address the problem of information overload by retrieving desired information for the user based on their preferences or those of similar users. The two main types of recommendation systems are personalized and non-personalized systems. Common techniques used include collaborative filtering, which finds users with similar tastes, and content-based filtering, which recommends items similar to those a user has liked based on item attributes.
Incorporating Diversity in a Learning to Rank Recommender System
Diversity is a desirable property of recommendations. Diversity can be increased with the use of re-rankers. This work presents an alternative approach where diversity is optimised together with accuracy during a matrix factorisation learning.
Recommender systems for E-commerce
This document discusses building a recommendation system for e-commerce. It begins by noting the importance of recommendations, with over 30% of online purchases coming from recommendations. It then discusses gathering data, both explicitly via ratings and reviews, and implicitly via user actions. Main approaches covered include content-based filtering, collaborative filtering using user-user and item-item similarities, and matrix factorization. The document also addresses challenges like sparsity, cold starts, scalability and privacy considerations in implementing recommendation systems.
Deep Learning for Recommender Systems - Budapest RecSys Meetup
1. Deep learning techniques such as convolutional neural networks, recurrent neural networks, and autoencoders can be applied to recommender systems.
2. Convolutional neural networks are commonly used to extract features from images, audio, and video that can then be used for recommendation. Recurrent neural networks can model user sessions as sequences of clicks.
3. Autoencoders learn lower-dimensional representations of items that capture similarities and can be used to make recommendations, especially for cold start problems where little is known about new users or items.
Recommendation system
The document discusses recommendation systems and machine learning models for recommendations. It covers the goals of recommendation systems, basic models including collaborative filtering, content-based, and knowledge-based systems. Neighborhood-based collaborative filtering is explained along with matrix factorization models. Deep learning methods for recommendations are also summarized, including neural collaborative filtering, graph-based models, and temporal models that handle dynamic graphs.
Deep Learning for Recommender Systems
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
Machine Learning for Recommender Systems MLSS 2015 Sydney
The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.
Calibrated Recommendations
This document proposes a calibrated recommendations approach that aims to provide recommendations that reflect all of a user's interests in correct proportions. Standard recommender systems trained for accuracy can lead to unbalanced recommendations that amplify a user's main interests and crowd out lesser interests. The calibrated recommendations approach uses a post-processing re-ranking step to optimize a submodular calibration metric, balancing accuracy and fairness by recommending items from all a user's interests in their correct proportions. Experiments on MovieLens data show that calibration can be improved significantly without degrading accuracy much.
Tutorial: Context In Recommender Systems
This document provides an overview of a tutorial on context-aware recommender systems. The tutorial will cover traditional recommendation techniques, context-aware recommendation which incorporates additional contextual information such as time and location, and context suggestion. It includes an agenda with topics, background information on recommender systems and evaluation metrics, and descriptions of techniques for context-aware recommendation including context filtering and modeling.
Boston ML - Architecting Recommender Systems
This document provides an overview of key concepts in recommender systems, including:
- The components of a recommender system including users, items, interactions, features, representations, predictions, loss functions, and learning.
- Design considerations for recommender systems such as choosing appropriate interaction values, features, representation functions, prediction functions, and loss functions.
- Examples of different types of recommender systems including collaborative filtering, content-based, hybrid, and real-world systems from Netflix, YouTube, and e-commerce.
- Tools for building recommender systems in Python like Implicit, Scikit-Learn, LightFM, TensorRec, and Annoy.
The document discusses the architecture
Content based filtering
This document discusses content-based recommendation techniques. It explains that content-based recommendation systems learn a user's preferences based on item attributes and characteristics to recommend similar items. It describes representing items and user profiles as vectors of keywords and computing similarity using metrics like cosine similarity. Finally, it briefly outlines probabilistic recommendation methods and linear classifiers for recommendations.
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Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Incorporating Diversity in a Learning to Rank Recommender System
Incorporating Diversity in a Learning to Rank Recommender System
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Deep Learning for Recommender Systems - Budapest RecSys Meetup
Machine Learning for Recommender Systems MLSS 2015 Sydney
Machine Learning for Recommender Systems MLSS 2015 Sydney
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This document summarizes and discusses recommender systems. It begins by defining recommender systems and their purpose of presenting personalized recommendations of items likely to interest users based on their profiles and preferences. It then outlines three main recommendation techniques: content-based filtering which uses item attributes to make recommendations; collaborative filtering which identifies similar users to make recommendations; and hybrid filtering which combines the two approaches. Finally, it discusses challenges for non-personalized recommendation systems in serving diverse user groups and notes that personalized approaches may help address this.
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Introduction to recommender systems
This document provides an introduction to recommender systems. It discusses how recommender systems can help users filter through large amounts of information and options in an era of information overload. It describes different types of recommender systems, including content-based, collaborative filtering, and context-based recommender systems. The document also discusses challenges like sparsity in data and scaling to large datasets, and how modeling approaches can help address these challenges.
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一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
毕业原版【微信:41543339】【(牛布毕业证书)牛津布鲁克斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(Deakin毕业证书)迪肯大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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如果您处于以下几种情况：
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留信网认证的作用:
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3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Recommendation systems
- 2. 2 What are Recommender Systems? Motivation behind these systems Why are they important? Examples? Amazon Shawshank Redemption RECOMMENDATIO N SYSTEMS
- 4. 4 Age group specific Gender Specific Region specific Popularity based Examples? Non-personalized RECOMMENDATION SYSTEMS
- 5. 5 Why are they required? Suppose person X likes Machine learning, Data Science but majority of the people like Cricket. Will Non-personalized recommenders be useful? Personalized RECOMMENDATION SYSTEMS
- 7. 7 TFIDF algorithm (Term Frequency*Inverse Document Frequency) Sample User Profiles: News Recommendations User profiling for content based RS 1. Likings Sports Entertainment Crime 2. Dislikings Politics International
- 8. 8 1. Titanic – Liked Romance – 0.5 Adventure – 0.3 Drama – 0.15 Other - 0.05 2. Avengers – Disliked Sci-fi – 0.3 Superhero – 0.4 Action – 0.2 Other – 0.1 Movie Recommendations
- 9. 9 User-User Similarity Suppose person X likes products A,B,C and person Y liked products A,B,D. Thus, the system will recommend C to X and D to Y. Collaborative filtering
- 10. 10 Hotel Recommender System – A Hybrid Approach 1. Generate/gather a dataset with features required 2. Take required inputs from user (The more the better !) 3. Classify hotels from the whole dataset according to user’s input 4. The Classified data is dealt with in two different methods a) Non personalized approach b) Personalized approach 5. In Non-Personalized approach the user is a first time user 6. Personalized approach considers regular users because their profiles are needed to be built.
- 11. 11 Input Data Classification Hotel Profiling Non- Personalized Rank boost Algorithm Hotel Profiles User Profile Personalized Correlations
- 12. 12 Collaborative Filtering Approach 1. Build profiles for every user 2. Correlate the required profile with others 3. The nearest neighbour or most correlated user will be similar to the target user 4. Consider hotels booked or reviewed or liked by these users to add bias to these hotels in classified hotel list for target user 5. Direct collaboration would be really difficult 6. Hybrid approach using bias is feasible
- 13. 13 Machine Learning approach Users WiFi Food Staff Location Review 1 0.4 0.5 0.7 0.6 *** 2 0.7 0.6 0.8 0.7 *** 3 0.5 0.8 0.5 0.9 **** 4 0.8 0.7 0.6 0.5 ? Features Label Recommended Hotel by our Hybrid system
- 14. 14 Activity
- 15. 15 Thank YOu