Cheil Turkey looks at various examples of dynamic pricing and explores its evolution into sympathetic pricing as a tactic brands use to develop a competitive edge and provide better value for their customers.
Cheil Turkey looks at various examples of dynamic pricing and explores its evolution into sympathetic pricing as a tactic brands use to develop a competitive edge and provide better value for their customers.
DISEÑO DE PAVIMENTO FLEXIBLE - MÉTODOS: AASHTO 93, INSTITUTO DEL ASFALTO, MEC...Engineerguy
En este trabajo, se demuestra cómo diseñar un pavimento flexible (asfalto), mediante los métodos AASHTO 93, Insituto del Asfalto, USACE, Índice de Grupo y Wyoming.
NOTA: DESCARGANDO EL ARCHIVO, EL TEXTO DEJA DE ESTAR UNIDO (TAL COMO SE MUESTRA EN LA PÁGINA WEB).
Top Reasons Why Java Rocks (report preview) - http:0t.ee/java-rocksZeroTurnaround
In this report we take a look at the top 10 reasons Java is the choice for so many development teams, all around the world, and why you should consider it for your next project. One of the main qualities Java possesses since the early design days is its simplicity. In that spirit we laid out in plain simple terms why Java outperforms other languages when it comes to popularity, performance, its amazing ecosystem and community.
For the less technical, this report explains the value Java brings to your projects. For fellow developers, we included some other projects in the Java ecosystem that you might want to look at. Check them out, have a play with them. It will make you a better developer.
Continue to the full report on RebelLabs: http:0t.ee/java-rocks
Consumer demand for Virtual and Augmented Reality devices is exponentially increasing. By 2020, both VR and AR market is expected to reach 150 bln$ with 55 million Head Mounted Displays (HMDs) available. This new technology is expected to heavily disrupt the gaming and entertainment businesses. But what does this new technology offer for today's media? And can we find new forms of storytelling?
***This is a presentation shown at Wan-Ifra World Publishing Expo 2015 on the Virtual Panel and Future of News Publishing panel in Hamburg ***
An overview of how change works, and what can be done to accelerate transformational change in an industry. Created for the Openlab Workshop, December 1-2, 2015 in Washington, DC.
논문 제목부터 재미있어 보이는 주제 입니다. 오늘 딥러닝 논문읽기 모임에서 소개드릴 논문은 DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems, 강화학습을 이용한 온라인 추천 시스템 입니다. 비공개 된 정보들이 몇가지가 있지만, 아이디어면에서 여러분들이 충분히 재밌게 들으실수 있습니다. 강화학습의 기본적인 개념부터,
논문에 대한 디테일하고 깊이 있는 리뷰를
펀디멘탈팀 김창연 님이 도와주셨습니다!
오늘도 많은 관심 미리 감사드립니다!
추가로 .. 딥러닝 논문읽기 모임은 청강방 오픈채팅 방을 운영하고 있습니다. 최근 악성 홍보 봇 계정이 늘어나 방을 비밀번호를 걸어두게 되었습니다
딥러닝 청강방도 많은 관심 부탁드립니다!
청강방 링크 : https://open.kakao.com/o/gp6GHMMc
청강방 비밀번호 : 0501
Using R in Kaggle Competitions.
Kaggle has been the most popular data science platform linking close to half a million of data scientists worldwide. How to get yourself a decent ranking on Kaggle competitions with R programming, eXtreme Gradient BOOSTing, and a laptop. Great machine learning tools for all levels to get started and learn. Find out how to perform features engineering, tuning XGB models, selecting a sizable cross validations and performing model ensembles.
Continuous Evaluation of Deployed Models in Production Many high-tech industr...Databricks
Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. When a problem first arises, it may go unnoticed for some time. Once it is noticed, investigating its underlying cause is a time-consuming, manual process. Wouldn’t it be great if the model’s output were automatically monitored? If they could be visualized, sliced by different dimensions? If the system could automatically detect performance degradation and trigger alerts? In this presentation, we describe our experience from building such a core machine-learning services: Model Evaluation.
Our service provides automated, continuous evaluation of the performance of a deployed model over commonly-used metrics like the area-under-the-curve (AUC), root-mean-square-error (RMSE) etc. In addition, summary statistics about the model’s output, their distributions are also computed. The service also provides a dashboard to visualize the performance metrics, summary statistics and distributions of a model over time along with REST APIs to retrieve these metrics programmatically.
These metrics can be sliced by input features (e.g. Geography, Product type) to provide insights into model performance over different segments. The talk will describe various components that are required in building such a service and metrics of interest. Our system has a backend component built with spark on Azure Databricks. The backend can scale to analyze TBs of data to generate model evaluation metrics.
We will talk about how we modified Spark MLLib for computing AUC sliced by different dimensions and other optimizations in Spark to improve compute and performance. Our front-end and middle-tier, built with Docker and Azure Webapp provides visuals and REST APIs to retrieve the above metrics. This talk will cover various aspects of building, deploying and using the above system.
Ayudando a los Viajeros usando 500 millones de Reseñas Hoteleras al MesBig Data Colombia
TrustYou analiza reseñas de hoteles en linea para crear un resumen para cada hotel en el mundo: http://www.trust-score.com/ ;
Los datos procesados por TrustYou son integrados por servicios como Kayak, Trivago, HipMunk entre otros.
Cada semana analizamos la web para descargar 3 millones de reviews. Estos son analizados usando técnicas de lingüística computacional, procesamiento de lenguaje natural y aprendizaje de maquinas. Para ello usamos casi exclusivamente Python. En esta charla Miguel les contará qué estrategias y herramientas usa TrustYou para lograr este objetivo.
Presentador: Miguel Fernando Cabrera. (@mfcabrera) Ing. de Sistemas e Informática de la Universidad Nacional de Colombia (Medellin), M.Sc. en Computer Science de la TU Munich con un Honours en Administración Tecnológica del CDTM. Fundó y lideró hasta principio del 2015 Munich DataGeeks, un grupo de interés en ML y Data Science más Baviera. Actualmente trabaja como como Data Engineer / Scientist para TrustYou.
Building efficient machine learning applications is not a simple task. The typical engineering process is an iteration of data wrangling, feature generation, model selection, hyperparameter tuning and evaluation. The amount of possible variations of input features, algorithms and parameters makes it too complex to perform efficiently even by experts. Automating this process is especially important when building machine learning applications for thousands of customers. In this talk I demonstrate how we build effective ML models using AutoML capabilities we develop at Salesforce. Our AutoML capabilities include techniques for automatic data processing, feature generation, model selection, hyperparameter tuning and evaluation. I present several of the implemented solutions with Scala and Spark.
DISEÑO DE PAVIMENTO FLEXIBLE - MÉTODOS: AASHTO 93, INSTITUTO DEL ASFALTO, MEC...Engineerguy
En este trabajo, se demuestra cómo diseñar un pavimento flexible (asfalto), mediante los métodos AASHTO 93, Insituto del Asfalto, USACE, Índice de Grupo y Wyoming.
NOTA: DESCARGANDO EL ARCHIVO, EL TEXTO DEJA DE ESTAR UNIDO (TAL COMO SE MUESTRA EN LA PÁGINA WEB).
Top Reasons Why Java Rocks (report preview) - http:0t.ee/java-rocksZeroTurnaround
In this report we take a look at the top 10 reasons Java is the choice for so many development teams, all around the world, and why you should consider it for your next project. One of the main qualities Java possesses since the early design days is its simplicity. In that spirit we laid out in plain simple terms why Java outperforms other languages when it comes to popularity, performance, its amazing ecosystem and community.
For the less technical, this report explains the value Java brings to your projects. For fellow developers, we included some other projects in the Java ecosystem that you might want to look at. Check them out, have a play with them. It will make you a better developer.
Continue to the full report on RebelLabs: http:0t.ee/java-rocks
Consumer demand for Virtual and Augmented Reality devices is exponentially increasing. By 2020, both VR and AR market is expected to reach 150 bln$ with 55 million Head Mounted Displays (HMDs) available. This new technology is expected to heavily disrupt the gaming and entertainment businesses. But what does this new technology offer for today's media? And can we find new forms of storytelling?
***This is a presentation shown at Wan-Ifra World Publishing Expo 2015 on the Virtual Panel and Future of News Publishing panel in Hamburg ***
An overview of how change works, and what can be done to accelerate transformational change in an industry. Created for the Openlab Workshop, December 1-2, 2015 in Washington, DC.
논문 제목부터 재미있어 보이는 주제 입니다. 오늘 딥러닝 논문읽기 모임에서 소개드릴 논문은 DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems, 강화학습을 이용한 온라인 추천 시스템 입니다. 비공개 된 정보들이 몇가지가 있지만, 아이디어면에서 여러분들이 충분히 재밌게 들으실수 있습니다. 강화학습의 기본적인 개념부터,
논문에 대한 디테일하고 깊이 있는 리뷰를
펀디멘탈팀 김창연 님이 도와주셨습니다!
오늘도 많은 관심 미리 감사드립니다!
추가로 .. 딥러닝 논문읽기 모임은 청강방 오픈채팅 방을 운영하고 있습니다. 최근 악성 홍보 봇 계정이 늘어나 방을 비밀번호를 걸어두게 되었습니다
딥러닝 청강방도 많은 관심 부탁드립니다!
청강방 링크 : https://open.kakao.com/o/gp6GHMMc
청강방 비밀번호 : 0501
Using R in Kaggle Competitions.
Kaggle has been the most popular data science platform linking close to half a million of data scientists worldwide. How to get yourself a decent ranking on Kaggle competitions with R programming, eXtreme Gradient BOOSTing, and a laptop. Great machine learning tools for all levels to get started and learn. Find out how to perform features engineering, tuning XGB models, selecting a sizable cross validations and performing model ensembles.
Continuous Evaluation of Deployed Models in Production Many high-tech industr...Databricks
Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. When a problem first arises, it may go unnoticed for some time. Once it is noticed, investigating its underlying cause is a time-consuming, manual process. Wouldn’t it be great if the model’s output were automatically monitored? If they could be visualized, sliced by different dimensions? If the system could automatically detect performance degradation and trigger alerts? In this presentation, we describe our experience from building such a core machine-learning services: Model Evaluation.
Our service provides automated, continuous evaluation of the performance of a deployed model over commonly-used metrics like the area-under-the-curve (AUC), root-mean-square-error (RMSE) etc. In addition, summary statistics about the model’s output, their distributions are also computed. The service also provides a dashboard to visualize the performance metrics, summary statistics and distributions of a model over time along with REST APIs to retrieve these metrics programmatically.
These metrics can be sliced by input features (e.g. Geography, Product type) to provide insights into model performance over different segments. The talk will describe various components that are required in building such a service and metrics of interest. Our system has a backend component built with spark on Azure Databricks. The backend can scale to analyze TBs of data to generate model evaluation metrics.
We will talk about how we modified Spark MLLib for computing AUC sliced by different dimensions and other optimizations in Spark to improve compute and performance. Our front-end and middle-tier, built with Docker and Azure Webapp provides visuals and REST APIs to retrieve the above metrics. This talk will cover various aspects of building, deploying and using the above system.
Ayudando a los Viajeros usando 500 millones de Reseñas Hoteleras al MesBig Data Colombia
TrustYou analiza reseñas de hoteles en linea para crear un resumen para cada hotel en el mundo: http://www.trust-score.com/ ;
Los datos procesados por TrustYou son integrados por servicios como Kayak, Trivago, HipMunk entre otros.
Cada semana analizamos la web para descargar 3 millones de reviews. Estos son analizados usando técnicas de lingüística computacional, procesamiento de lenguaje natural y aprendizaje de maquinas. Para ello usamos casi exclusivamente Python. En esta charla Miguel les contará qué estrategias y herramientas usa TrustYou para lograr este objetivo.
Presentador: Miguel Fernando Cabrera. (@mfcabrera) Ing. de Sistemas e Informática de la Universidad Nacional de Colombia (Medellin), M.Sc. en Computer Science de la TU Munich con un Honours en Administración Tecnológica del CDTM. Fundó y lideró hasta principio del 2015 Munich DataGeeks, un grupo de interés en ML y Data Science más Baviera. Actualmente trabaja como como Data Engineer / Scientist para TrustYou.
Building efficient machine learning applications is not a simple task. The typical engineering process is an iteration of data wrangling, feature generation, model selection, hyperparameter tuning and evaluation. The amount of possible variations of input features, algorithms and parameters makes it too complex to perform efficiently even by experts. Automating this process is especially important when building machine learning applications for thousands of customers. In this talk I demonstrate how we build effective ML models using AutoML capabilities we develop at Salesforce. Our AutoML capabilities include techniques for automatic data processing, feature generation, model selection, hyperparameter tuning and evaluation. I present several of the implemented solutions with Scala and Spark.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
talk at KTH 14 May 2014 about matrix factorization, different latent and neighborhood models, graphs and energy diffusion for recommender systems, as well as what makes good/bad recommendations.
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15MLconf
Many Shades of Scale: Big Learning Beyond Big Data: In the machine learning research community, much of the attention devoted to ‘big data’ in recent years has been manifested as development of new algorithms and systems for distributed training on many examples. This focus has led to significant advances in the field, from basic but operational implementations on popular platforms to highly sophisticated prototypes in the literature. In the meantime, other aspects of scaling up learning have received relatively little attention, although they are often more pressing in practice. The talk will survey these less-studied facets of big learning: scaling to an extremely large number of features, to many components in predictive pipelines, and to multiple data scientists collaborating on shared experiments.
Similar to Appraiser : How Airbnb Generates Complex Models in Spark for Demand Prediction (20)
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
7. • Modeling goal: Predict the
probability of a booking for each price
• Scale
- O(Million) of derived features
- Over 5B training data points.
Proportional to (# listings) x (# days)
Modeling
Approach
and Scale
8. • Demand (seasonality, events)
• Listing location (market, neighborhood,
street block)
• Listing type and quality
What Affects
Prices?
13. Listing
Type&Quality
Entire apt or a room
Amenities
Guest reviews Private room with
view of Eiffel Tower
Entire home/ houseboat
across from Eiffel Tower
19. Black box
Random forest
Q: Will I get a book?
Lat :37.7841593
Long :-122.4031865
Price : 500
Latitude > 37.7?
Longitude >-143.0 ? Price > 213 ?
1.0
-1.0
• Difficult to interpret
• 1000s of trees in a forest
• Not clear relationship between variables and target
20. • Control quantization
• Control interaction (crosses)
• O(millions) of sparse parameters
• O(tens) active any timeGlass box model
Lat :37.7841593
Long :-122.4031865
Price : 500
(37.7,-122.4) AND Price in [500, 550]
Good location High price!
22. • Write training data once
• Iterate feature transforms on the fly
• Control quantization
• Control interaction (crosses)
• Debuggable models (graphs + readable)
(20.6,-87.0) ^ Sabbia = Good!
Benefits of
feature
transforms
28. L1 regularization
• Starts learning ID like features
• Exact location
• Time of creation
• Damages Splines
!
For Linear model:
L1 + L2 + Random Dropout
!
For Spline model:
L_infinity spline group norm +
Change of basis (Bézier Cubic-4 params) +
Dropout for spline model
Overfitting
Dangers of high
capacity models
32. KD Trees
Bodies of water kept
separate
Min Sum P(leaf_i |
parent) * n_leaf_i
33. Generate Features
• RGB, HSV, Edge histograms,
Texture histograms
Sparsify
• Winner Take All Hash
(max of random elements)
Ranking Loss
Image Ranking
Human curated vs. organic bookings
Ornate
Living Rooms
vs.
Creature
Comforts