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Awesome Machines Compilation | Best Images Collections HD For Gadget windows Mac Android
Awesome Equipment Compilation Awesome machines compilation Awesome Equipment Compilation
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yesterday by wotek
Human Or Machine? The Incredibly Life Like Android Robots From Japan | Best Images Collections HD For Gadget windows Mac Android
Human or Equipment? The Amazingly Lifestyle-Like Android Robots From Japan A new exhibition entitled “Android: What is Human?” launching tomorrow at the Countrywide Museum of Emerging Science and Innovation (Miraikan) in Tokyo will showcase some of the most realistic humanoid robots that have ever been noticed. Human or Equipment? The Amazingly Lifestyle-Like Android Robots From […]
IFTTT  WordPress  Iphone  android  (Character  Species)  robots  Hiroshi  Ishiguro  (Film  Actor)  humanoid  Japanese  Robotics  Machine  (Product  Category)  Miraikan  (Museum) 
2 days ago by wotek
Der Original DeLorean Nachbau Aus ZURÜCK IN DIE ZUKUNFT | Best Images Collections HD For Gadget windows Mac Android
Der first DeLorean Nachbau aus ZURÜCK IN DIE ZUKUNFT Auf der CeBit 2016 in Hannover konnte guy in Halle 13 einen first Nachbau des DeLorean aus “Zurück in die Zukunft” (eng. “Again To The Upcoming”) bestaunen. Die Zeitmaschine hat ein junger Tüftler aus Erlangen erbaut und kann auch für Veranstaltungen unter www.lease-a-delo.de gemietet werden. Bericht: […]
IFTTT  WordPress  Car  1985  auto  Back  to  the  future  delo...  Delorean  Flux  Kompensator  Fluxkompen...  General  Motors  Gmc  Ii  Iii  Time  Machine  Zeitmaschine  zurück  in  die  zukunft 
2 days ago by wotek
Statistics for Monitoring: Anomaly Detection (Part 2)
Experimental anomaly detection methods based on autocorrelation and non-parametric 2 sample tests. Autocorrelation helps distinguishing between metrics that have changing behavior and stable ones.
Archive  machine  learning 
2 days ago by ronert
Statistics for Monitoring: Load Testing (Tuning)
Shows how simple statistical methods can help clean obtained data and find bottlenecks for load testing. Usually there is a goal for a load testing otherwise why do that.
Archive  machine  learning 
2 days ago by ronert
Statistics for Monitoring: Anomaly Detection (Part 1)
Introduces control charts based methods for production anomaly detection. It’s a number of closed tcp sockets per second. One system crashed and a lot of clients got disconnected which resulted in large spike on the graph.
Archive  machine  learning 
2 days ago by ronert
Statistics for Monitoring: Data Properties
Introduces performance metrics and their properties that affect choice of algorithms for anomaly detection, performance analysis, capacity planning. There will be several examples that illustrate typical properties and anomalies. Here's the way typical performance metrics look like.
Archive  machine  learning 
2 days ago by ronert
Statistics for Monitoring: Preface
I’m capturing my talks about statistics and performance monitoring given at HighLoad++ 2013 conference and other events in a readable form.
Archive  machine  learning 
2 days ago by ronert
Statistics for Monitoring: Correlation and Clustering
Finding metrics with similar behavior and analyzing internal system dependencies. There are a lot of situations when you see an unexpected change in one metric (e.g. increased latency or error rate) and need to find the cause.
Archive  machine  learning 
2 days ago by ronert
Rapid A/B-testing with Sequential Analysis | Audun M Øygard
A common issue with classical A/B-tests, especially when you want to be able to detect small differences, is that the sample size needed can be prohibitively large. In many cases it can take several weeks, months or even years to collect enough data to conclude a test.
Archive  machine  learning 
2 days ago by ronert
Bayesian optimisation for smart hyperparameter search - Tim Head
Fitting a single classifier does not take long, fitting hundreds takes a while. To find the best hyperparameters you need to fit a lot of classifiers. What to do?
Archive  machine  learning  python 
2 days ago by ronert
Taxi Trajectory Winners’ Interview: 1st place, Team ? | No Free Hunch
Taxi Trajectory Prediction was the first of two competitions that we hosted for the 2015 ECML PKDD conference on machine learning. Team ? took first place using deep learning tools developed at the MILA lab where they currently study.
Archive  machine  learning 
2 days ago by ronert
Hinton's Dropout in 3 Lines of Python - i am trask
Summary: Dropout is a vital feature in almost every state-of-the-art neural network implementation. This tutorial teaches how to install Dropout into a neural network in only a few lines of Python code.
Archive  deep  learning  machine 
2 days ago by ronert
Number plate recognition with Tensorflow - Matt's ramblings
Over the past few weeks I’ve been dabbling with deep learning, in particular convolutional neural networks. One standout paper from recent times is Google’s Multi-digit Number Recognition from Street View.
Archive  deep  learning  machine  python  tensorflow 
2 days ago by ronert
Searching for Approximate Nearest Neighbours | Lyst Engineering Blog
Nearest neighbour search is a common task: given a query object represented as a point in some (often high-dimensional) space, we want to find other objects in that space that lie close to it.
Archive  machine  learning 
2 days ago by ronert
CrowdFlower Winners’ Interview: 3rd place, Team Quartet | No Free Hunch
The goal of the CrowdFlower Search Results Relevance competition was to come up with a machine learning algorithm that can automatically evaluate the quality of the search engine of an e-commerce site. Given a query (e.g.
Archive  machine  learning 
2 days ago by ronert

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