Had a great time discussing Azure ML with the Denver R User Group meeting this evening. We covered ways to use R in Azure ML to clean, transform and process data and to build machine learning models. This is a great meetup if you have a chance to attend a future meeting!
Natural Language in Human-Robot InteractionSeokhwan Kim
Tutorial @ The 4th International Conference on Human-Agent Interaction (HAI 2016), 04 Oct 2016
Speakers: Rafael E. Banchs, Seokhwan Kim, Luis Fernando D’Haro, Andreea I. Niculescu
Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th.
Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow.
Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience.
Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
Had a great time discussing Azure ML with the Denver R User Group meeting this evening. We covered ways to use R in Azure ML to clean, transform and process data and to build machine learning models. This is a great meetup if you have a chance to attend a future meeting!
Natural Language in Human-Robot InteractionSeokhwan Kim
Tutorial @ The 4th International Conference on Human-Agent Interaction (HAI 2016), 04 Oct 2016
Speakers: Rafael E. Banchs, Seokhwan Kim, Luis Fernando D’Haro, Andreea I. Niculescu
Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th.
Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow.
Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience.
Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
Interesting Observations (7 Sins of Programmers); The compiler is to blame; Archeological strata; The last line effect; Programmers are the smartest; Security, security! But do you test it?; You can’t know everything; Seeking a silver bullet.
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A new static analysis tool for C++ code CppCat was presented just recently. You probably heard a lot about the previous product (PVS-Studio) by the same authors. I was pretty doubtful about it then: on the one hand, static analysis is definitely a must-have methodology - things go better with than without it; on the other hand, PVS-Studio may scare users off with its hugeness, an enterprise-like character and the price, of course. I could imagine a project team of 50 developers buying it but wasn't sure about single developers or small teams of 5 developers. I remember suggesting to the PVS-Studio authors deploying "PVS as a cloud service" and sell access to it by time. But they chose to go their own way and created an abridged version at a relatively small price (which any company or even a single developer can afford).
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The caret package is a unified interface to a large number of predictive mode...odsc
The caret package is a unified interface to a large number of predictive model functions in R.
First created in 2005, the home for the source code and documentation has changed several times.
In this talk, we will outline the somewhat unique aspects of the package and how it impacts the development environment (including documentation and testing). Friction points with CRAN and their resolution will also be discussed.
AWS Compute Overview: Servers, Containers, Serverless, and Batch | AWS Public...Amazon Web Services
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Good has won this time. To be more exact, source codes of the Chromium project have won. Chromium is one of the best projects we have checked with PVS-Studio.
Good has won this time. To be more exact, source codes of the Chromium project have won. Chromium is one of the best projects we have checked with PVS-Studio.
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ManageIQ currently runs on Ruby on Rails 3. Aaron "tenderlove" Patterson presents his effort to migrate to RoR 4, which entails some changes in the code to take advantage of the latest advances in RoR.
For more on ManageIQ, see http://manageiq.org/
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This article demonstrates capabilities of the static code analysis methodology. The readers are offered to study the samples of one hundred errors found in open-source projects in C/C++.
A new static analysis tool for C++ code CppCat was presented just recently. You probably heard a lot about the previous product (PVS-Studio) by the same authors. I was pretty doubtful about it then: on the one hand, static analysis is definitely a must-have methodology - things go better with than without it; on the other hand, PVS-Studio may scare users off with its hugeness, an enterprise-like character and the price, of course. I could imagine a project team of 50 developers buying it but wasn't sure about single developers or small teams of 5 developers. I remember suggesting to the PVS-Studio authors deploying "PVS as a cloud service" and sell access to it by time. But they chose to go their own way and created an abridged version at a relatively small price (which any company or even a single developer can afford).
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11. Cleaning
80% of any data scientists time is spent cleaning the data
leaving just 20% to complain about cleaning the data
https://www.flickr.com/photos/derekgavey/4283300990
13. Cleaning
Missing values
Normalization
Scaling
Filtering
https://www.flickr.com/photos/derekgavey/4283300990
Signal Processing
Complex Models
Simple Thresholds
Smoothing
Moving Average
14. Cleaning
Missing values
Normalization
Scaling
Filtering
Meta data and naming
https://www.flickr.com/photos/derekgavey/4283300990
Column Names
Projection
Type Cleanups
15. Splits
Training
Testing
Validation
https://www.flickr.com/photos/tabor-roeder/11606138806
80%
20%
e.g. when Comparing different models
22. Scoring
Apply model to your data
Outputs:
Result
The probability that the result is sort of right
23. Demo time…
Azure Portal -> Machine Learning Studio
New Experiment
https://www.flickr.com/photos/mgdtgd/144569790
24. Evaluating
Is my classifier any good?
False Negative
True Positive
False Positive True Negative
Precision: TP/(TP+FP)
Accuracy
(TP+TN)/(P+N)
25. Evaluating
How far out was I?
Error distance functions:
Mean squared error
Mean absolute error
R2 Coefficient of Determination
https://www.flickr.com/photos/dahlstroms/3945656390