Predictive analytics intro for business leaders | BA4ALL 2017 by Tropos.io
•
1 like•75 views
Tropos and Textgain collaborated to host a hands-on workshop at Business Analytics insight 2017. This deck shows only the intro part of the workshop.
Report
Share
Report
Share
Recommended
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...Tata Consultancy Services
If insights are available from mass amounts of data, you require enormous agility across business units to act on these. Understand how your peers tackle such problems and what new approaches are available to businesses.Ben Analytics Introduction to Predictive Analytics
Introduction to predictive analytics, data science, machine learning and tools you can use to get started.
Introduction To Data Science With Python
Data Science is one of the hottest career options globally right now with data scientists earning an average of 15 lacs to 18 lacs annually. This deck explains the fundamentals of Data Science, the role of a Data Scientist.
The deck also introduces the Certificate Masterclass in Data Science with Python by Spotle Learn. This course is specifically designed by the experts for the people who want to build a career in data science. This course will equip you with the fundamental knowledge and practical expertise required for data science careers through a rigorous pedagogy based on videos, live projects, interactive classes and integrated internships.
Göteborg university(condensed)
3 hours presentation to Göteborg University systemvetarprogram students - condensed version
opinionmining-131221011849-phpapp02-converted.ppt
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Recommended
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...Tata Consultancy Services
If insights are available from mass amounts of data, you require enormous agility across business units to act on these. Understand how your peers tackle such problems and what new approaches are available to businesses.Ben Analytics Introduction to Predictive Analytics
Introduction to predictive analytics, data science, machine learning and tools you can use to get started.
Introduction To Data Science With Python
Data Science is one of the hottest career options globally right now with data scientists earning an average of 15 lacs to 18 lacs annually. This deck explains the fundamentals of Data Science, the role of a Data Scientist.
The deck also introduces the Certificate Masterclass in Data Science with Python by Spotle Learn. This course is specifically designed by the experts for the people who want to build a career in data science. This course will equip you with the fundamental knowledge and practical expertise required for data science careers through a rigorous pedagogy based on videos, live projects, interactive classes and integrated internships.
Göteborg university(condensed)
3 hours presentation to Göteborg University systemvetarprogram students - condensed version
opinionmining-131221011849-phpapp02-converted.ppt
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts.
Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy.
In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
Does big data = big insights?
I presented this at ICT Spring Europe 2015 in Luxembourg. The presentation highlights the way in which big data investments are not always delivering on their promise and why brands should consider taking a 'human-centred' approach to big data analytics.
Machine learning at b.e.s.t. summer university
The outline of our course on supervised machine learning.
-
Laszlo kovacs
Arpad Fulop
Introduction-to-Data-Science.pdf
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Customer insights for product strategy
The talk I gave at WebExpo 2014 in Prague! Slides only.
Here is the abstract:
Usability testing, focus groups, interviews, contextual inquiry, customer development - there are many names and techniques for gathering insights from your users, your customers. In recent years, agile software development and lean startup have changed how research is conducted, and have raised awareness of how important it is to understand who you are building your products for.
In this talk, Johanna will cover best practices for gathering insights in the context of product development. Her session will address questions such as:
* What techniques are best at the early stage of a product?
* What exactly is customer development and how is it different?
* What are the skills you need to turn research results into actionable insights that inform your product strategy?
Johanna will share her own story of being a researcher and product manager, how and why her practice has changed, and provide actionable advice on embedding research in your process.
Introduction-to-Knowledge Discovery in Database
this will introduce you about Knowledge Discovery in Database
Data Mining With Big Data
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
Machine Learning Crash Course 2017 - Genova - DIBRIS - IIT - MIT
My slide deck at the Machine Learning Crash Course 2017 - Genova - DIBRIS - IIT - MIT
Webinar: Will the Real AI Please Stand Up?
In this webinar, Interset CTO Stephan Jou and VP of Products Mario Daigle discussed what to look for when cybersecurity vendors claim to leverage AI for UEBA. View a recording of this webinar at https://zoom.us/webinar/register/WN_0Etv6kilRN-0QuqoNn26bg.
Investigating Performance: Design & Outcomes with xAPI | LSCon 2017
First delivered as a Learning Solutions 'Data and Measurement' Track Conference Session on March 22, 2017 by Janet Laane-Effron and Sean Putman.
Find out more about HT2 Labs' research and development at HT2Labs.com
Artificial Intelligence: Evolution and its Impact on Marketing
In one real-life minute, Google receives over 4 million searches, 2.5 million pieces of content are shared on Facebook, and Pandora users listen to 61 thousand hours of music. The amount of data that is produced in a day is massive that the world has began to turn to artificial intelligence to make use of this data. Read here to learn about the way that artificial intelligence is revolutionizing the use of big data and how this will impact the world of marketing and business.
BIG DATA AND MACHINE LEARNING
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Juliette Melton - Mobile User Experience Research
Most user experience research takes place sitting behind a computer. And yet these days, most networked experiences are happening on mobile devices. Some common user experience research methods work well in a mobile environment — others don’t. In this talk, Juliette Melton will guide you through how to use some great existing research methods in a mobile context, how to incorporate some new (and fun!) methods into your arsenal, and propose next generation tools and services to make mobile user experience research even better.
Juliette has ten years of experience building, managing, and researching digital environments and is a human factors researcher based at IDEO in San Francisco. She’s deeply interested in the intersections between digital culture, learning, and communication. Her work has spanned a broad range of industries including social media, casual gaming, education administration, electronic publishing, corporate banking, computer hardware, and public health.
Community education — through workshops, lectures, and writing — is an important part of her work. Remote user experience methods, agile project management, and research program planning are frequent topics.
Juliette holds an MEd from the Technology, Innovation, and Education program at the Harvard Graduate School of Education where she focused on developing models for innovative networked learning applications. She also has a BA in Comparative Literature from Haverford College.
Follow Juliette on Twitter: @j
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
More Related Content
Similar to Predictive analytics intro for business leaders | BA4ALL 2017 by Tropos.io
Does big data = big insights?
I presented this at ICT Spring Europe 2015 in Luxembourg. The presentation highlights the way in which big data investments are not always delivering on their promise and why brands should consider taking a 'human-centred' approach to big data analytics.
Machine learning at b.e.s.t. summer university
The outline of our course on supervised machine learning.
-
Laszlo kovacs
Arpad Fulop
Introduction-to-Data-Science.pdf
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Customer insights for product strategy
The talk I gave at WebExpo 2014 in Prague! Slides only.
Here is the abstract:
Usability testing, focus groups, interviews, contextual inquiry, customer development - there are many names and techniques for gathering insights from your users, your customers. In recent years, agile software development and lean startup have changed how research is conducted, and have raised awareness of how important it is to understand who you are building your products for.
In this talk, Johanna will cover best practices for gathering insights in the context of product development. Her session will address questions such as:
* What techniques are best at the early stage of a product?
* What exactly is customer development and how is it different?
* What are the skills you need to turn research results into actionable insights that inform your product strategy?
Johanna will share her own story of being a researcher and product manager, how and why her practice has changed, and provide actionable advice on embedding research in your process.
Introduction-to-Knowledge Discovery in Database
this will introduce you about Knowledge Discovery in Database
Data Mining With Big Data
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
Machine Learning Crash Course 2017 - Genova - DIBRIS - IIT - MIT
My slide deck at the Machine Learning Crash Course 2017 - Genova - DIBRIS - IIT - MIT
Webinar: Will the Real AI Please Stand Up?
In this webinar, Interset CTO Stephan Jou and VP of Products Mario Daigle discussed what to look for when cybersecurity vendors claim to leverage AI for UEBA. View a recording of this webinar at https://zoom.us/webinar/register/WN_0Etv6kilRN-0QuqoNn26bg.
Investigating Performance: Design & Outcomes with xAPI | LSCon 2017
First delivered as a Learning Solutions 'Data and Measurement' Track Conference Session on March 22, 2017 by Janet Laane-Effron and Sean Putman.
Find out more about HT2 Labs' research and development at HT2Labs.com
Artificial Intelligence: Evolution and its Impact on Marketing
In one real-life minute, Google receives over 4 million searches, 2.5 million pieces of content are shared on Facebook, and Pandora users listen to 61 thousand hours of music. The amount of data that is produced in a day is massive that the world has began to turn to artificial intelligence to make use of this data. Read here to learn about the way that artificial intelligence is revolutionizing the use of big data and how this will impact the world of marketing and business.
BIG DATA AND MACHINE LEARNING
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Juliette Melton - Mobile User Experience Research
Most user experience research takes place sitting behind a computer. And yet these days, most networked experiences are happening on mobile devices. Some common user experience research methods work well in a mobile environment — others don’t. In this talk, Juliette Melton will guide you through how to use some great existing research methods in a mobile context, how to incorporate some new (and fun!) methods into your arsenal, and propose next generation tools and services to make mobile user experience research even better.
Juliette has ten years of experience building, managing, and researching digital environments and is a human factors researcher based at IDEO in San Francisco. She’s deeply interested in the intersections between digital culture, learning, and communication. Her work has spanned a broad range of industries including social media, casual gaming, education administration, electronic publishing, corporate banking, computer hardware, and public health.
Community education — through workshops, lectures, and writing — is an important part of her work. Remote user experience methods, agile project management, and research program planning are frequent topics.
Juliette holds an MEd from the Technology, Innovation, and Education program at the Harvard Graduate School of Education where she focused on developing models for innovative networked learning applications. She also has a BA in Comparative Literature from Haverford College.
Follow Juliette on Twitter: @j
Similar to Predictive analytics intro for business leaders | BA4ALL 2017 by Tropos.io (20)
Machine Learning Crash Course 2017 - Genova - DIBRIS - IIT - MIT
Machine Learning Crash Course 2017 - Genova - DIBRIS - IIT - MIT
Investigating Performance: Design & Outcomes with xAPI | LSCon 2017
Investigating Performance: Design & Outcomes with xAPI | LSCon 2017
Artificial Intelligence: Evolution and its Impact on Marketing
Artificial Intelligence: Evolution and its Impact on Marketing
Recently uploaded
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Malana- Gimlet Market Analysis (Portfolio 2)
A market analysys on Spotify's parent Podcast company Gimlet.
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(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【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！
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
原版定制【微信:41543339】【(Bradford毕业证书)布拉德福德大学毕业证】【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！
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Object oriented system analysis and design
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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
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
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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
Q1’2024 Update: MYCI’s Leap Year Rebound
Key things you need to know on consumer confidence, key behavioral, tech, e-wallet & esports trends in Malaysia.
一比一原版(NYU毕业证)纽约大学毕业证成绩单
NYU毕业证【微信95270640】《如何办理NYU毕业证纽约大学文凭学历》【Q微信95270640】《纽约大学文凭学历证书》《纽约大学毕业证书与成绩单样本图片》毕业证书补办 Fake Degree做学费单《毕业证明信-推荐信》成绩单，录取通知书，Offer，在读证明，雅思托福成绩单，真实大使馆教育部认证，回国人员证明，留信网认证。网上存档永久可查！
【本科硕士】纽约大学纽约大学毕业证学位证（GPA修改）；学历认证（教育部认证）；大学Offer录取通知书留信认证使馆认证；雅思语言证书等高仿类证书。
办理流程：
1客户提供办理纽约大学纽约大学毕业证学位证信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
真实网上可查的证明材料
1教育部学历学位认证留服官网真实存档可查永久存档。
2留学回国人员证明（使馆认证）使馆网站真实存档可查。
我们对海外大学及学院的毕业证成绩单所使用的材料尺寸大小防伪结构（包括：纽约大学纽约大学毕业证学位证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）都有原版本文凭对照。质量得到了广大海外客户群体的认可同时和海外学校留学中介做到与时俱进及时掌握各大院校的（毕业证成绩单资格证结业证录取通知书在读证明等相关材料）的版本更新信息能够在第一时间掌握最新的海外学历文凭的样版尺寸大小纸张材质防伪技术等等并在第一时间收集到原版实物以求达到客户的需求。
本公司还可以按照客户原版印刷制作且能够达到客户理想的要求。有需要办理证件的客户请联系我们在线客服中心微信：95270640 或咨询在线已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了一天那一天父亲陪着山娃辗转长隆水上乐园疯了一整天水上漂流高空冲浪看大马戏大凡里面有的父亲都带着他去疯一把山娃算了算这一次足足花了老爸元够他挣上半个月的山娃很不解一向节俭的父亲啥时变得如此阔绰大方大把大把掏钱时居然连眉头也不皱一下车票早买好了直达卧铺车得经子
Recently uploaded (20)
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...
Influence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business Plan
Data_and_Analytics_Essentials_Architect_an_Analytics_Platform.pptx
Data_and_Analytics_Essentials_Architect_an_Analytics_Platform.pptx
Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
The affect of service quality and online reviews on customer loyalty in the E...
The affect of service quality and online reviews on customer loyalty in the E...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Predictive analytics intro for business leaders | BA4ALL 2017 by Tropos.io
- 1. Predictive analytics Guy De Pauw guy@textgain.com Joris Van den Borre
- 2. What kind of person bought these groceries?
- 4. Human brains generalize insights from repetition
- 5. Raw history of outcomes we want to predict Raw history data of causes (CRM, logs, text, pics) 1. Build the decision rules 2. Predict ! New event or history data Predictions and accuracy Algorith m Automate learning based on recorded data
- 6. Case: predict consumer segment using deep learning Preference for specialty coffee Preferenceforbioveggies Hipster Partyflock Preference for specialty coffee Preferenceforbioveggies Hipster Partyflock I only drink specialty coffee but don’t care much for bio veggies With the data we have We can build a model To predict someone’s segment
- 7. Chopping Kanye: object recognition on webscale data
- 8. Algoritm s & frameworks Research & know-how Capacit y Open-source CLOU D Compute Storage 3 focus areas for predictive analytics … Methodology
Editor's Notes
- http://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=spiral®Dataset=reg-plane&learningRate=0.01®ularizationRate=0&noise=0&networkShape=7,7,7&seed=0.53126&showTestData=false&discretize=false&percTrainData=50&x=false&y=false&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=true&cosY=false&sinY=true&collectStats=false&problem=classification&initZero=false&hideText=false