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Everybody uses price promotions in retail. However, individual pricing is seldom used, particularly in offline retail. Marketing literature has been advocating the use of individual price discrimination for decades. Furthermore, product recommendations, ever-present in e-commerce, are also not often found in offline retail. We show the machine learning driven system behind a new promotion channel that enables retailers and manufacturers alike to target individual customers in offline retail. Lessons learned, technologies used, and machine learning approaches driving our system will be shown. Daniel Guhl has a background in economics & marketing, and got interested in data modeling during his Ph.D.. Currently, he is working as a data scientist at a Berlin based Start-up and is pursuing a postdoc at Humboldt University. He enjoys learning everyday and focuses on solving real world problems.
Revolutionizing Offline Retail Pricing & Promotions with ML - Daniel Guhl @ P...
Revolutionizing Offline Retail Pricing & Promotions with ML - Daniel Guhl @ P...
PAPIs.io
Deep Learning (DL) is becoming a big tsunami in the Machine Learning community. This talk aims at introducing DL, its motivation and main techniques. However, part of this talk is also devoted to demystify DL. What are the main advantages but also the main drawbacks of DL?. And what are the key issues that the practitioners have to consider? Roberto Paredes is an Associate Professor at Departamento de Sistemas Informáticos y Computación DSIC of the Universidad Poliécnica de Valencia UPV. He belongs to the Pattern Recognition and Human Language Technologies Research Centre PRHLT. Roberto Paredes is the Director of the PRHLT and the President of the Spanish AERFAI Association. His main research interests are around the statistical learning, machine learning and more recently neural networks and deep learning.
Demystifying Deep Learning - Roberto Paredes Palacios @ PAPIs Connect
Demystifying Deep Learning - Roberto Paredes Palacios @ PAPIs Connect
PAPIs.io
The best services have one thing in common: a superb customer experience. Banking services are no exception to this rule, and indeed the quest for an effortless, well informed, and personalized customer experience is one of the main goals of today's innovation in digital banking services. According to what Maslow has described in his "pyramid of needs", customers are seeking a more intimate and meaningful experience where banking services can actively assist the customer in performing and managing their financial life. Predictive APIs have a fundamental role in all this, as they enable a new set of customer journeys such as automatic categorization of transactions, detecting and alerting recurrent payments, pre-approving credit requests or provide better tools to fight fraud without limiting legitimate customer transactions. In this talk, I will focus on how to provide better banking services by using predictive APIs. I will describe the path on how to get there and the challenges of implementing predictive APIs in a strictly audited and regulated domain such as banking. Finally, I will briefly introduce a number of data science techniques to implement those customer journeys and describe how big/fast data engineering can be used to realize predictive data pipelines. Natalino is currently Enterprise Data Architect at ING in the Netherlands, where leads the strategy, definition, design and implementation of big/fast data solutions for data-driven applications, for personalized marketing, predictive analytics, and fraud/security management. All-round Software Architect, Data Technologist, Innovator, with 15+ years experience in research, development and management of distributed architectures and scalable services and applications.
Predictive APIs: What about Banking? - Natalino Busa @ PAPIs Connect
Predictive APIs: What about Banking? - Natalino Busa @ PAPIs Connect
PAPIs.io
Fintech startups are taking business away from traditional institutions like banks, exchanges, and brokerages. One of the reasons that these startups are able to compete with $30B+ behemoths like Credit Suisse and Goldman Sachs is their advanced decision making capabilities. By leveraging new data sources and better predictive analytics, companies like Ferratum Bank can make more accurate decisions in a fraction of the time. This talk will cover: Types of decisions you can automate Challenges in building predictive, financial apps First-hand, real-world examples Greg Lamp is the co-Founder and CTO of Yhat. In this role, Greg leads development of Yhat's core products and infrastructure and is the principal architect of the company's cloud and on-premise enterprise software applications. Greg was previously a product manager at OnDeck, a fintech startup in New York and before that an analyst at comScore. Greg is a graduate of the University of Virginia.
Microdecision making in financial services - Greg Lamp @ PAPIs Connect
Microdecision making in financial services - Greg Lamp @ PAPIs Connect
PAPIs.io
What is the future we want to create, and what can we do – starting today – to actively shape that future with general AI? This talk outlines a vision for the future of humankind once AI reaches human or superhuman levels, and leads the audience through the steps one research group is taking to get there. From the economics of smart robots and job replacement, to bionic humans exploring the universe through space travel, the talk offers a window into the work of 30 researchers focused on AI development and safety, and explains what attendees can do themselves to help make that future happen. JoEllen is the AI Safety Ambassador and Head of PR for GoodAI, a Prague-based general AI research and development company. A high school teacher by trade, she has a bachelor’s degrees in English and Philosophy from Seattle University, a master’s degree in Transatlantic Studies from Charles University in Prague, and is the recipient of Fulbright grant. JoEllen is particularly interested in how AI will affect international government and political relations.
Engineering the Future of Our Choice with General AI - JoEllen Lukavec Koeste...
Engineering the Future of Our Choice with General AI - JoEllen Lukavec Koeste...
PAPIs.io
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. In this talk we'll show how to use an AWS Spark cluster to train a model quickly from a laptop at a very little cost (around 10€). Vincent Van Steenbergen is a freelance (big) data engineer who's working on a range of international projects, implementing systems able to handle terabytes of data, usually involving Spark, Scala, Kafka, Hadoop and Cassandra. His main interest right now is applying these techniques to solve machine learning problems. Vincent was previously a technical architect at Property. Works, a real estate startup in London and before that an R&D engineer at IDAaaS.
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...
PAPIs.io
Shopping, or as the people on the other side of the counter call it, retail has become the number one breeding ground for predictive applications in the enterprise. What started as simple recommendation engines has evolved into a complex and powerful ecosystem of predictive applications that affect core processes such as pricing, replenishment and staff planning. In this talk, Ulrich Kerzel will share impact and experiences from building and operating predictive applications for large retailers, and explain why the future of retail is as much a science as an art. Dr. Ulrich Kerzel is a Senior data scientists at Blue Yonder and renowned scientist with research experience at the University of Cambridge and CERN. Ulrich Kerzel earned his PhD under Professor Dr Feindt at the US Fermi National Laboratory and at that time made a considerable contribution to core technology of NeuroBayes. After his PhD, he went to the University of Cambridge, were he was a Senior Research Fellow at Magdelene College. His research work focused on complex statistical analyses to understand the origin of matter and antimatter using data from the LHCb experiment at the Large Hadron Collider at CERN, the world’s biggest research institute for particle physics. He continued this work as a Research Fellow at CERN before he came to Blue Yonder as a senior data scientist.
How to predict the future of shopping - Ulrich Kerzel @ PAPIs Connect
How to predict the future of shopping - Ulrich Kerzel @ PAPIs Connect
PAPIs.io
We live in a world of data, of big data. A big portion of this data has been generated by humans, and particularly through their mobile phones. In fact, there are almost as many mobile phones in the world as humans. The mobile phone is the piece of technology with the highest levels of adoption in human history. We carry them with us all through the day (and night, in many cases), leaving digital traces of our physical interactions. Mobile phones have become sensors of human activity in the large scale and also the most personal devices. In my talk, I will present some of the work that we are doing at Telefonica Research in the area of human behavior understanding from data captured with mobile phones, and particularly our work in the area of Big Data for Social Good. I will highlight opportunities but also challenges that we would need to address in order to truly leverage this opportunity. Nuria Oliver is a computer scientist and Scientific Director at Telefónica. She holds a Ph.D. from the Media Lab at MIT. She is one of the most cited female computer scientist in Spain, with her research having been cited by more than 8900 publications. She is well known for her work in computational models of human behavior, human computer-interaction, intelligent user interfaces, mobile computing and big data for social good.
The emergent opportunity of Big Data for Social Good - Nuria Oliver @ PAPIs C...
The emergent opportunity of Big Data for Social Good - Nuria Oliver @ PAPIs C...
PAPIs.io
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Feature engineering — HJ Van Veen (Nubank) @@PAPIs Connect — São Paulo 2017
Feature engineering — HJ Van Veen (Nubank) @@PAPIs Connect — São Paulo 2017
Extracting information from images using deep learning and transfer learning ...
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Deep learning for sentiment analysis — André Barbosa (elo7) @PAPIs Connect — ...
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Building machine learning service in your business — Eric Chen (Uber) @PAPIs ...
Building machine learning applications locally with Spark — Joel Pinho Lucas ...
Building machine learning applications locally with Spark — Joel Pinho Lucas ...
Battery log data mining — Ramon Oliveira (Datart) @PAPIs Connect — São Paulo ...
Battery log data mining — Ramon Oliveira (Datart) @PAPIs Connect — São Paulo ...
A tensorflow recommending system for news — Fabrício Vargas Matos (Hearst tv)...
A tensorflow recommending system for news — Fabrício Vargas Matos (Hearst tv)...
Scaling machine learning as a service at Uber — Li Erran Li at #papis2016
Scaling machine learning as a service at Uber — Li Erran Li at #papis2016
Real-world applications of AI - Daniel Hulme @ PAPIs Connect
Real-world applications of AI - Daniel Hulme @ PAPIs Connect
Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantar...
Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantar...
Revolutionizing Offline Retail Pricing & Promotions with ML - Daniel Guhl @ P...
Revolutionizing Offline Retail Pricing & Promotions with ML - Daniel Guhl @ P...
Demystifying Deep Learning - Roberto Paredes Palacios @ PAPIs Connect
Demystifying Deep Learning - Roberto Paredes Palacios @ PAPIs Connect
Predictive APIs: What about Banking? - Natalino Busa @ PAPIs Connect
Predictive APIs: What about Banking? - Natalino Busa @ PAPIs Connect
Microdecision making in financial services - Greg Lamp @ PAPIs Connect
Microdecision making in financial services - Greg Lamp @ PAPIs Connect
Engineering the Future of Our Choice with General AI - JoEllen Lukavec Koeste...
Engineering the Future of Our Choice with General AI - JoEllen Lukavec Koeste...
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...
How to predict the future of shopping - Ulrich Kerzel @ PAPIs Connect
How to predict the future of shopping - Ulrich Kerzel @ PAPIs Connect
The emergent opportunity of Big Data for Social Good - Nuria Oliver @ PAPIs C...
The emergent opportunity of Big Data for Social Good - Nuria Oliver @ PAPIs C...
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一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
wsppdmt
Data analysis tasks.
Data Analyst Tasks to do the internship.pdf
Data Analyst Tasks to do the internship.pdf
theeltifs
Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Valters Lauzums
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Lecture_2_Deep_Learning_Overview-newone1
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Federal Constitution of the Swiss Confederation of 18 April 1999 (Status as of 1 January 2024)
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Switzerland Constitution 2002.pdf.........
EfruzAsilolu
Gartners Model
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Gartner's Data Analytics Maturity Model.pptx
chadhar227
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怎样办理纽约州立大学宾汉姆顿分校毕业证(SUNY-Bin毕业证书)成绩单学校原版复制
怎样办理纽约州立大学宾汉姆顿分校毕业证(SUNY-Bin毕业证书)成绩单学校原版复制
vexqp
In my capstone project, I investigated the impact of COVID-19 on education. Using data analysis and statistical methods, I explored various aspects such as enrollment trends, access to resources, and socioeconomic disparities. I found a significant association between children missing classes and a lack of internet connection at home, as well as between household financial situations and children's enrollment in school. These findings highlight the importance of addressing disparities in internet access, household finances, and geographical location to ensure equal educational opportunities for all students during and beyond the pandemic.
Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION
Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION
LakpaYanziSherpa
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Thanks again
7. Epi of Chronic respiratory diseases.ppt
7. Epi of Chronic respiratory diseases.ppt
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