Effective Numerical Computation in NumPy and SciPyKimikazu Kato
Presented at PyCon JP 2014.
Video is available at
http://bit.ly/1tXYhw6
This talk explores case studies of effective usage of Numpy/Scipy and shows that the computational speed sometimes improves drastically with the appropriate derivation of formulas and performance-conscious implementation. I especially focus on scipy.sparse, the module for sparse matrices, which is often useful in the areas of machine learning and natural language processing.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
Presented at PyCon JP 2014.
Video is available at
http://bit.ly/1tXYhw6
This talk explores case studies of effective usage of Numpy/Scipy and shows that the computational speed sometimes improves drastically with the appropriate derivation of formulas and performance-conscious implementation. I especially focus on scipy.sparse, the module for sparse matrices, which is often useful in the areas of machine learning and natural language processing.
This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
A talk from Toronto's FITC Spotlight on Hardware talk. I spoke about using tools like Openframeworks, OpenCV, and the Kinect to create Interactive Installations, and paired it with an interactive lighting installation.
References, citations, and source code can be found here: http://www.andrewlb.com/2013/06/sls-notes/
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
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Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
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Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
QCon Rio - Machine Learning for EveryoneDhiana Deva
Já não são mais necessários supercomputadores e times de PhDs do MIT para a criação de modelos preditivos baseados em dados. Estamos presenciando inovações em Aprendizado de Máquina que estão tornando este campo cada vez mais acessível.
Esta palestra tem como objetivo desmistificar o aprendizado de máquina, através da exposição de conceitos e uso de uma série de tecnologias.
Serão abordados os tipos de problemas desta área(classificação, regressão, clusterização, redução de dimensionalidade, etc.), suas as etapas (normalização, treinamento, otimização, regularização, etc.) e seus algoritmos, desde regressão linear, k-means, passando por árvores de decisão e até redes neurais, sempre aplicadas a problemas reais.
Na palestra, também conheceremos ferramentas como Sckit-learn, Pandas, R, MATLAB e Amazon Machine Learning, além de uma forma para praticar e experimentar estas ideias através de competições como o Kaggle.
This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
A talk from Toronto's FITC Spotlight on Hardware talk. I spoke about using tools like Openframeworks, OpenCV, and the Kinect to create Interactive Installations, and paired it with an interactive lighting installation.
References, citations, and source code can be found here: http://www.andrewlb.com/2013/06/sls-notes/
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
---------------------------------------------------------------
Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
---------------------------------------------------------------
Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
QCon Rio - Machine Learning for EveryoneDhiana Deva
Já não são mais necessários supercomputadores e times de PhDs do MIT para a criação de modelos preditivos baseados em dados. Estamos presenciando inovações em Aprendizado de Máquina que estão tornando este campo cada vez mais acessível.
Esta palestra tem como objetivo desmistificar o aprendizado de máquina, através da exposição de conceitos e uso de uma série de tecnologias.
Serão abordados os tipos de problemas desta área(classificação, regressão, clusterização, redução de dimensionalidade, etc.), suas as etapas (normalização, treinamento, otimização, regularização, etc.) e seus algoritmos, desde regressão linear, k-means, passando por árvores de decisão e até redes neurais, sempre aplicadas a problemas reais.
Na palestra, também conheceremos ferramentas como Sckit-learn, Pandas, R, MATLAB e Amazon Machine Learning, além de uma forma para praticar e experimentar estas ideias através de competições como o Kaggle.
Integrated Web Recommendation Model with Improved Weighted Association Rule M...ijdkp
World Wide Web plays a significant role in human life. It requires a technological improvement to satisfy
the user needs. Web log data is essential for improving the performance of the web. It contains large,
heterogeneous and diverse data. Analyzing g the web log data is a tedious process for Web developers,
Web designers, technologists and end users. In this work, a new weighted association mining algorithm is
developed to identify the best association rules that are useful for web site restructuring and
recommendation that reduces false visit and improve users’ navigation behavior. The algorithm finds the
frequent item set from a large uncertain database. Frequent scanning of database in each time is the
problem with the existing algorithms which leads to complex output set and time consuming process. The
proposed algorithm scans the database only once at the beginning of the process and the generated
frequent item sets, which are stored into the database. The evaluation parameters such as support,
confidence, lift and number of rules are considered to analyze the performance of proposed algorithm and
traditional association mining algorithm. The new algorithm produced best result that helps the developer
to restructure their website in a way to meet the requirements of the end user within short time span.
Searching is one of the important operations in computer science. Retrieving information from
huge databases takes a lot of processing time to get the results. The user has to wait till the completion
of processing to find whether search is successful or not. In this research paper, it provides a detailed
study of Binary Search and how the time complexity of Binary Search can be reduced by using Odd
Even Based Binary Search Algorithm, which is an extension of classical binary search strategy. The
worst case time complexity of Binary Search can be reduced from O(log2N) to O(log2(N-M)) where
N is total number of items in the list and M is total number of even numbers if search KEY is ODD
or M is total number of odd numbers if search KEY is EVEN. Whenever the search KEY is given, first
the KEY is determined whether it is odd or even. If given KEY is odd, then only odd numbers from
the list are searched by completely ignoring list of even numbers. If given KEY is even, then only
even numbers from the list are searched by completely ignoring list of odd numbers. The output of
Odd Even Based algorithm is given as an input to Binary Search algorithm. Using Odd Even Based
Binary Search algorithm, the worst case performances in Binary Search algorithm are converted
into best case or average case performance. Therefore, it reduces total number of comparisons, time
complexity and usage of various computer resources.
Hydraulics now a days is a very distinguished area which has lot of major challenges often came in its
progress due to the realistic changes affecting on applicable working fluid viz. Water. Most occasions,
Water can be easily available but in certain times it may be scarce also. The available water vary according
to its properties. It exists in normal conditions as well as salty or hardy due to deposits. Majority of Water
is contaminated with minerals, dust or dirt. Often pure water which may be acidic or alkaline can be used
for making discharges through the Turbines
A Future for R: Parallel and Distributed Processing in R for Everyoneinside-BigData.com
In this deck from the 2018 European R Users Meeting, Henrik Bengtsson from the University of California San Francisco presents: A Future for R: Parallel and Distributed Processing in R for Everyone.
In this video from the European R Users Meeting, Henrik Bengtsson from the University of California San Francisco presents: A Future for R: Parallel and Distributed Processing in R for Everyone.
"The future package is a powerful and elegant cross-platform framework for orchestrating asynchronous computations in R. It's ideal for working with computations that take a long time to complete; that would benefit from using distributed, parallel frameworks to make them complete faster; and that you'd rather not have locking up your interactive R session."
Watch the video: https://wp.me/p3RLHQ-jJ4
Learn more: https://blog.revolutionanalytics.com/2019/01/future-package.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
As the amount of metrics, software that produce and process them, and people involved in them continue to increase, we need better ways to organize them, to make them self-describing, and do so in a way that is consistent. Leveraging this, we can then automatically build graphs and dashboards, given a query that represents an information need, even for complicated cases. We can build richer visualizations, alerting and fault detection. This talk will introduce the concepts and related tools, demonstrate possibilities using the Graph-Explorer interface, and lay the groundwork for future work.
Monitoring Complex Systems: Keeping Your Head on Straight in a Hard WorldBrian Troutwine
This talk will provide motivation for the extensive instrumentation of complex computer systems and make the argument that such systems. This talk will provide practical starting points in Erlang projects and maintain a perspective on the human organization around the computer system. Brian will focus on getting started with instrumentation in a systematic way and follow up with the challenge of interpreting and acting on metrics emitted from a production system in a way which does not overwhelm operators’ ability to effectively control or prioritize faults in the system. He’ll use historical examples and case studies from my work to keep the talk anchored in the practical.
Talk objectives:
Brian hopes to convince the audience of two things:
* that monitoring and instrumentation is an essential component of any long-lived system and
* that it's not so hard to get started, after all.
He’ll keep a clear-eyed view of what works and is difficult in practice so that the audience can make a reasoned decision after the talk.
Target audience:
This talk would appeal to engineers with long-running production employments, operations folks and Erlangers in general.
These are slides from the Dec 17 SF Bay Area Julia Users meeting [1]. Ehsan Totoni presented the ParallelAccelerator Julia package, a compiler that performs aggressive analysis and optimization on top of the Julia compiler. Ehsan is a Research Scientist at Intel Labs working on the High Performance Scripting project.
[1] http://www.meetup.com/Bay-Area-Julia-Users/events/226531171/
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Pooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
Benchy: Lightweight framework for Performance Benchmarks Marcel Caraciolo
Benchy: Lightweight framework for Performance Benchmarks on Python Scripts.
Presented at XXVI Pernambuco Python User Group Meeting at Recife, Pernambuco, Brazil on 06.04.2013
Malo Denielou - No shard left behind: Dynamic work rebalancing in Apache BeamFlink Forward
http://flink-forward.org/kb_sessions/no-shard-left-behind-dynamic-work-rebalancing-in-apache-beam/
The Apache Beam (incubating) programming model is designed to support several advanced data processing features such as autoscaling and dynamic work rebalancing. In this talk, we will first explain how dynamic work rebalancing not only provides a general and robust solution to the problem of stragglers in traditional data processing pipelines, but also how it allows autoscaling to be truly effective. We will then present how dynamic work rebalancing works as implemented in Google Cloud Dataflow and which path other Apache Beam runners link Apache Flink can follow to benefit from it.
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Accelerating the Development of Efficient CP Optimizer ModelsPhilippe Laborie
The IBM Constraint Programming optimization system CP Optimizer was designed to provide automatic search and a simple modeling of discrete optimization problems, with a particular focus on scheduling applications. It is used in industry for solving operational planning and scheduling problems. We will give an overview of CP Optimizer and then describe in further detail a set of features such as input/output file format, warm-start or conflict refinement that help accelerate the development of efficient models.
How Google AppEngine deals with digital art? how about music? a few case studies developed by Stinkdigital with Google Creative Lab and how App Engine dealt with a considerable amount of visits
3 things you must know to think reactive - Geecon Kraków 2015Manuel Bernhardt
Over the past few years, web-applications have started to play an increasingly important role in our lives. We expect them to be always available and the data to be always fresh. This shift into the realm of real-time data processing is now transitioning to physical devices, and Gartner predicts that the Internet of Things will grow to an installed base of 26 billion units by 2020.
As reactive architectures gain in popularity, more and more developers find themselves faced with the challenge of "thinking reactive". To leave behind the well-known concepts of mutable, object-oriented, imperative and synchronous programming in favour of immutable, functional, declarative and asynchronous programming requires quite a mind shift and it isn't obvious to take the plunge.
In this talk we will explore three concepts from the world of functional programming that are at the core of building reactive applications: immutability, higher-order functions and manipulating immutable collections. We will first see how the "traditional" mutable, object-oriented approach of doing things can be problematic when it comes to multi-core programming, and then how to apply them to asynchronous systems.
Aiming at complete code coverage by unit tests tends to be cumbersome, especially for cases where external API calls a part of the code base. For these reasons, Python comes with the unittest.mock library, appearing to be a powerful companion in replacing parts of the system under test.
Similar to Model-based GUI testing using UPPAAL (20)
3. CISS Focus Areas Applikationer Teknologi Værktøj Modeller Metoder Protokoller Design- og Prog.sprog Operativ system HW platform GPS Open source Home automation Mobile robotter Intelligente sensorer Ad hoc netværk Mobiltlf Audio/Video Konsum elektr Kontrolsystemer Automobile X-by wire Algoritmik SW-udvikling Effektforbrug Pålidelighed Test & Validering Hybride systemer Kommunikationsteori Model Based Development of Embedded Software Intelligent Sensor Networks Embedded & RT Platform LAB Safety Critical Software Systems Embedded System Testing & Verification HW/SW Co-Design, Design Space Exploration Resource Optimal Scheduling Security High Level Programming Languages for ES IT in Automation