google machine learning api

Support original, more content Welcome to the author blog:http://www.china10s.com/blog/?p=490 Machine learning This method of calculation has been known to the world in the last century, but it has not been developed because of the computer-limited computing power and network speed. With the Moore effect, the current computer performance has soared, even in the hands of the iphone, than the United States on

-eng.utoronto.ca/~datamining/DataMiningCourse.htm Data mining technology in engineering, Torontohttp://sli.ics.uci.edu/Classes/2011W-178 machine learning and data mining, University of California, IrvineKnowledge discovery of http://users.csc.calpoly.edu/~dekhtyar/466-Fall2010/data, Carpolihttp://ttic.uchicago.edu/~gregory/courses/LargeScaleLearning/a large University of Chicago study,http://www.cise.ufl.ed

represent the neural network layer, which is very efficient for linear algebra and similar to Numpy arrays. Decaf Decaf is a deep learning library recently released by UC Berkeley. In the challenges of Imagenet classification, it is found that its neural network implementation is very advanced (state of art ). Nolearn If you want to use the excellent Scikit-learn library API in deep

engine for large-scale data processing, and for some applications, such as machine learning, Spark is 100 times times faster than Hadoop MapReduce. Apache Spark's fast-track table explains the big data ecosystem and describes common behaviors and actions.Https://dzone.com/refcardz/apache-sparkScala Cheatsheets 1Scala Cheatsheets 2Scala from Dzone Reference CardSpark Cheatsheet on GitHubScala on Spark cheat

Loading data converting data Feature Extraction/Engineering Configuring the Learning Model Training model Use well-trained models (such as getting predictions) Pipelines provide a standard API for using machine learning models. This makes it easier to switch a model during testing and

PHP-ML is a machine learning library written using PHP. While we know that Python or C + + provides more machine learning libraries, in fact, most of them are slightly more complex and configured to be desperate for many novices. PHP-ML This machine

engine invokes the Oncompletion.oncompletion () callback method provided by the client programmer. Can be set by calling the Mediaplayer.setoncompletionlistener (Oncompletionlistener) method. The internal playback engine once called the Oncompletion.oncompletion () callback method, indicating that the MediaPlayer object entered the playbackcompleted state. 9.3) when in playbackcompleted state, you can call the start () method again to let the MediaPlayer object enter the started state again. A

["Predictions"] = []# Loop over the results and add them to the list of# returned predictions for(Imagenetid, label, prob)inchresults[0]: R = {"Label": Label,"Probability":float(Prob)} data["Predictions"].append (R)# indicate that's the request was a successdata["Success"] =True# Return the data dictionary as a JSON responsereturnFlask.jsonify (data)Although it is a core part, it is very easy to be reused. is the process of reading the data and then processing it. # If This is the main thread o

In-depth spark machine learning combat (user behavior analysis)Course View Address: http://www.xuetuwuyou.com/course/144The course out of self-study, worry-free network: http://www.xuetuwuyou.comI. Objectives of the courseMaster the various operations of sparksql in-depth understanding of spark's internal implementation principlesLearn more about the construction and operation of various algorithmic models

information gain Building a decision Tree Random Forest K Nearest neighbor--an algorithm of lazy learning Summarize The fourth chapter constructs a good training set---data preprocessing Handling Missing values Eliminate features or samples with missing values Overwrite missing values Understanding the Estimator API in Sklearn Working with

linear algebra and similar to numpy arrays.DecafDecaf is a recent deep learning library published by UC Berkeley, tested in the Imagenet Classification challenge, and its neural network implementation is very advanced (state of art).NolearnIf you want to use the excellent Scikit-learn Library API in deep learning, encapsulating the decaf Nolearn will make it eas

neural networks through different configuration files. IX, Hebel hebel is a neural network library with GPU support that can determine the properties of a neural network through YAML files. Provides a way to separate the Divine Network and code-friendly, and run the model quickly, it is written in pure Python, is a very friendly library, but because of the development soon, on the depth and the vast, there is some lack! ten, Neurolab neurolab is an API