This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/oxLZZMR1lVY
Description:
Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy, especially for time-series problems.
With Driverless AI, data scientists of all proficiency levels can train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client API from Python. Driverless AI builds hundreds or thousands of models under the hood to select the best feature engineering and modeling pipeline for every specific problem such as churn prediction, fraud detection, real-estate pricing, store sales prediction, marketing ad campaigns and many more.
To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware. For example, Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on premise. Driverless AI is fully supported on all major cloud providers.
There are two more product innovations in Driverless AI: statistically rigorous automatic data visualization and machine learning interpretability with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and the models.
In this talk, we explain how Driverless AI works and show how easy it is to reach top 5% rankings for several highly competitive Kaggle competitions. (edited)
Speaker's Bio:
Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.
Helping data scientists escape the seduction of the sandbox - Krish Swamy, We...Sri Ambati
This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/xc3j20Om3UM
Description:
Data science is indeed one of the sexy jobs of the 21st century. But it is also a lot of hard work. And the hard work is seldom about the math or the algorithms. It is about building relevant machine learning products for the real world. We will go over some of the must-haves as you take your machine learning model out of the sandbox and make it work in the big, bad world outside.
Speaker's Bio:
Krish Swamy is an experienced professional with deep skills in applying analytics and BigData capabilities to challenging business problems and driving customer insights. Krish's analytic experience includes marketing and pricing, credit risk, digital analytics and most recently, big data analytics and data transformation. His key experiences lie in banking and financial services, the digital customer experience domain, with a background in management consulting. Other key skills include influencing organizational change towards a data and analytics driven culture, and building teams of analysts, statisticians and data scientists.
Predicting Medical Test Results using Driverless AISri Ambati
This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/n9g9GxIJoT4
Description:
The goal of the research was to develop an approach to predict individual medical test results based on longitudinal medical and pharma claims data without direct lab measures using data-driven techniques. Such discoveries may result in improved treatment strategies. In the presentation we demonstrate how Driverless AI was used both for estimating highly accurate model and results explanations.
Speaker's Bio:
Alexander is the Data Science leader at poder.IO. He is responsible for data flow architecture and insight mining, all powered by machine learning. Before joining poder.IO, Alexander made an academic career at Belarusian State University and Minsk Innovation University where he was Head of the Informatics and Mathematics Department.
Scalable Automatic Machine Learning with H2OSri Ambati
In this presentation, Parul Pandey, will provide a history and overview of the field of “Automatic Machine Learning” (AutoML), followed by a detailed look inside H2O’s open source AutoML algorithm. H2O AutoML provides an easy-to-use interface which automates data pre-processing, training and tuning a large selection of candidate models (including multiple stacked ensemble models for superior model performance). The result of the AutoML run is a “leaderboard” of H2O models which can be easily exported for use in production. AutoML is available in all H2O interfaces (R, Python, Scala, web GUI) and due to the distributed nature of the H2O platform, can scale to very large datasets. The presentation will end with a demo of H2O AutoML in R and Python, including a handful of code examples to get you started using automatic machine learning on your own projects.
Parul's Bio:
Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science, evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.
This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/oxLZZMR1lVY
Description:
Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy, especially for time-series problems.
With Driverless AI, data scientists of all proficiency levels can train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client API from Python. Driverless AI builds hundreds or thousands of models under the hood to select the best feature engineering and modeling pipeline for every specific problem such as churn prediction, fraud detection, real-estate pricing, store sales prediction, marketing ad campaigns and many more.
To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware. For example, Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on premise. Driverless AI is fully supported on all major cloud providers.
There are two more product innovations in Driverless AI: statistically rigorous automatic data visualization and machine learning interpretability with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and the models.
In this talk, we explain how Driverless AI works and show how easy it is to reach top 5% rankings for several highly competitive Kaggle competitions. (edited)
Speaker's Bio:
Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.
Helping data scientists escape the seduction of the sandbox - Krish Swamy, We...Sri Ambati
This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/xc3j20Om3UM
Description:
Data science is indeed one of the sexy jobs of the 21st century. But it is also a lot of hard work. And the hard work is seldom about the math or the algorithms. It is about building relevant machine learning products for the real world. We will go over some of the must-haves as you take your machine learning model out of the sandbox and make it work in the big, bad world outside.
Speaker's Bio:
Krish Swamy is an experienced professional with deep skills in applying analytics and BigData capabilities to challenging business problems and driving customer insights. Krish's analytic experience includes marketing and pricing, credit risk, digital analytics and most recently, big data analytics and data transformation. His key experiences lie in banking and financial services, the digital customer experience domain, with a background in management consulting. Other key skills include influencing organizational change towards a data and analytics driven culture, and building teams of analysts, statisticians and data scientists.
Predicting Medical Test Results using Driverless AISri Ambati
This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/n9g9GxIJoT4
Description:
The goal of the research was to develop an approach to predict individual medical test results based on longitudinal medical and pharma claims data without direct lab measures using data-driven techniques. Such discoveries may result in improved treatment strategies. In the presentation we demonstrate how Driverless AI was used both for estimating highly accurate model and results explanations.
Speaker's Bio:
Alexander is the Data Science leader at poder.IO. He is responsible for data flow architecture and insight mining, all powered by machine learning. Before joining poder.IO, Alexander made an academic career at Belarusian State University and Minsk Innovation University where he was Head of the Informatics and Mathematics Department.
Scalable Automatic Machine Learning with H2OSri Ambati
In this presentation, Parul Pandey, will provide a history and overview of the field of “Automatic Machine Learning” (AutoML), followed by a detailed look inside H2O’s open source AutoML algorithm. H2O AutoML provides an easy-to-use interface which automates data pre-processing, training and tuning a large selection of candidate models (including multiple stacked ensemble models for superior model performance). The result of the AutoML run is a “leaderboard” of H2O models which can be easily exported for use in production. AutoML is available in all H2O interfaces (R, Python, Scala, web GUI) and due to the distributed nature of the H2O platform, can scale to very large datasets. The presentation will end with a demo of H2O AutoML in R and Python, including a handful of code examples to get you started using automatic machine learning on your own projects.
Parul's Bio:
Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science, evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
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.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERS
Resume
1. QI WEN
145 Medhurst Dr ∙ Ottawa ∙ Ontario ∙ K2G 4J9 ∙ 613-709-7377 ∙ wenqi2036@gmail.com
https://ca.linkedin.com/pub/qi-wen/66/359/871
SUMMARY OF QUALIFICATION
• New Graduate, fast learner, able to handle tasks independently and cooperatively.
• Excellent with C++ and has outstanding understanding in matlab, and Python
• Good knowledge in JavaScript, PHP,JSON, AJAX, JQuery, HTML5, CSS3, Ubuntu, Clearcase and
TCP/IP protocol (OSI Model) and MySQL
• Knowledge in Unity, Git, Composer, Codeception, Junit, PHPunit , selenium, XAMPP, phpmyadmin,
FileZilla, apache, Test Driven Development (TDD), Parallel Computing, Software design pattern, agile
development, source control
• Familiar with C, JAVA (J2SE), Eclipse, Android Studio, embedded programming and UML,
postgreSQL (PGadmin4), VisualSVNServer, Jasper, Wingware, Odoo 10, Putty, AWS (Amazon Web
Service)
EXPERIENCE
Innovision Consulting Inc. 2016.07-Present
Software Developer
Web development and Data analysis for landis International Inc.
Web development and Data management for Reseau Des Services De Sante En Francais (RSSFE)
Tutor 2015.09-2016.06
Tiger Tutor.
Teaching the first and second year computer engineering and math courses
Software Developer 2014.05-2014.08
Nearest Inc.
Creating new API and add new features to webpage using JavaScript & JQuery
Implemented "Login System" which authenticate users
Implemented "Fee payment system" deal with payment and special offer
Wrote Serve Side Testing Scripts to make sure system work properly (GIT, PHP, PHPunit,
codeception, and Composer)
Software Developer 2011.09-2011.12
Ericsson Inc.
Automation and Preinstall
Debugging and writing testing cases for QA teams by using Erlang
Familiar with Clear-Case and package delivering procedure
EDUCATION
Master in computer engineering 2013.09-2015.06
Carleton University, Ottawa, ON, Canada
Bachelor in Electrical engineering 2008.09-2013.06
Carleton University, Ottawa, ON, Canada
2. PROJECTS
RSSFE:
Create new API for RSSFE
Implemented new features on both client side (Javascript & Jquery) and server side (PHP &
Codeception).
Landis International Inc:
Apply data analysis on custom provided data set and upload the result to database.
Change the layout of webpage and fix bugs
Data Analytical:
A literature review on Big Data Processing Technologies includes Data Mining and Map
Reducing.
Locality-Aware Schedule Algorithm (LASA) for Hadoop - MapReduce Scheduler. LASA can
improve the performance (5%) and reduce cost (8%).
Fourth Year Project: Parallel Simulation of Transmission Line on VLSI (Matlab)
Mathematic Modeling
Modeled Transmission line
Written Script program (Matlab) to simulated the transmission line
Used multi-core distributes system to optimize the simulation process
Java:
“Football Team”: Built Artificial Intelligence and simulated soccer player.
“Image Processing”: Get rid of noise and recover the image from damage
Banking System and GUI using Swing
Applying Test Driven Development (TDD) by using Junit
C++:
“Students Information Database”: Data management program allows users to store, retrieve,
modify and delete student information by name, age or student number.
"Anti-Aircraft": Small game that allows users control an aircraft to destroy all enemy aircrafts
Python:
Maintaining and updating system for Landis International Inc.
"Picture Processing": add/remove fog, add/remove sunlight, color enhancement, remove red eyes
and so forth
Unity 3D (Javascript):
A 3D game is teaching student basic ideas of algorithms such as Stack, Queue, BS Tree
As a team leader to build software developing environment and construct software architecture
Making schedule and pushing teammates finish their part of job before deadline
Zigbee:
Written driver for Xbee (Zigbee) using C. Zigbee is a short range wireless communication system.