udacity python machine learning

evaluation under spark streaming. If you're a Scala, Java, or Python developer interested in machine learning and data analytics, and want to use the spark framework for large-scale application of common machine learning technologies, this book is written for you.It's a goo

Learning notes for "Machine Learning Practice": Draw a tree chart use a decision tree to predict the contact lens type, The decision tree is implemented in the previous section, but it is only implemented using a nested dictionary containing tree structure information. Its representation is difficult to understand. Obviously, it is necessary to draw an intuitiv

"%(Classifierresult, datinglabels[i]))if(Classifierresult! = Datinglabels[i]): Errorcount + = 1.0Print("The total error rate is:%f"% (errorcount/float (numtestvecs))) Print(Errorcount)defimg2vector (filename): Returnvect= Zeros ((1,1024)) Fr=open (filename) forIinchRange (32): Linestr=Fr.readline () forJinchRange (32): Returnvect[0,32*I+J] =Int (linestr[j])returnReturnvectdefhandwritingclasstest (): Hwlabels=[] trainingfilelist= Listdir ('trainingdigits')#load the training setm =Len (trainingfil

Http://www.cnblogs.com/bmsl/p/dongbin_bmsl_02.html By reading code to learn, always the most direct and fast. This chapter will explain the code for the first level of slim directory Tensorflow/tensorflow/contrib/slim/python/slim. This layer of code mainly includes learning.py, evaluation.py, summary.py, queue.py and model_analyzer.py, respectively corresponding to the model training, testing, logging, queue Management and Model Analysis section. Let

brain. 17.JSAT is a quick-start machine learning Library. The library was developed in my spare time, based on the GPL3 issue. Some of the content in the library can be learned autonomously, for example, all code is independent. Jsat has no external dependencies and is written in pure java. N-dimensional Arrays for Java (nd4j) is a scientific computing library for the JVM. They are used in a production env

examples. Algorithms of the Intelligent Web (Smart Web algorithm) PDFAuthor Haralambos Marmanis, Dmitry Babenko. The formula in this book is a little bit more than "collective intelligence programming", the example of which is mostly the application on the Internet, to see the name. The disadvantage is that the matching code inside is BeanShell and not python or anything else. In general, this book is still suitable for beginners, and the same need

The algorithm realization of decision tree in the case of commodity purchasing ability forecastBai NingsuDecember 24, 2016 22:05:42 absrtact: with the upsurge of machine learning and deep learning, all kinds of books abound. However, the majority is the basic theory knowledge Introduction, lacks the realization the thorough understanding. This series of

(GitHub 695 stars) Link: Https://github.com/facebookresearch/MUSE No.2 Deep-photo-styletransfer: Code and data for Deep photo Style Transfer, Cornell University Fujun Luan (GitHub 9747 stars) Link: https://github.com/luanfujun/deep-photo-styletransfer No.3 Face recognition: The simplest Python command line facial recognition API from Adam geitgey (GitHub 8672 stars) Link: https://github.com/ageitgey/face_recognition Content reference to: Open s

distance weighting which Realize(1)C + + version http://blog.csdn.net/mimi9919/article/details/51172095(2)Python version can refer to machine learning combat2. Perception MachineAlgorithm Realize(1)C + + version http://blog.csdn.net/idmer/article/details/493653013. Naive BayesAlgorithm Realize(1)C + + version http://b

would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a , the index data along the given axis in sorted order. Returns an array of subscripts after a small to large order. Axis represents the dimension to compare, which defaults to the last dimension.　Some function learning in 2.pythonThe reload () function, which needs to be i

The upcoming Apache Spark 2.0 will provide a machine learning model persistence capability. The persistence of machine learning models (the preservation and loading of machine learning models) makes the following three types of

improve performance; Thirdly, the learning model with parameter optimization can be used to predict the output of related problems. Driverless cars, new word discovery, etc. all have machine learning applications.Generally, the mathematical basis of machine learning mainly

TensorFlow learning --- getting started (1) ----- MNIST machine learning, References: http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html Data: http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_download.html Environment: windows + Python3.5 + tensorflow Python code From tensorflow. examples. tu

from:http://www.erogol.com/broad-view-machine-learning-libraries/Http://www.slideshare.net/VincenzoLomonaco/deep-learning-libraries-and-rst-experiments-with-theanoFebruary 6, EREN 1 COMMENT Especially, with the advent of many different and intricate machine learning algorit