best machine learning books 2017

1 Introduction 1.1 Wrong idea of machine learning Be sure to know a lot about Python programming and Python syntax Learn more about the theory and parameters of machine learning algorithms used by Scikit learn Avoid or have no access to other parts of the actual project.It may be applicable to some peo

despair. His style of being alone has influenced my view of the whole Tibetan minority, and there is no place to respect it. I thought, "I don't think I slept again tonight." ”I just climb out of bed straight start open source work, document open-source to GitHub a lot of ways, direct use of GitHub Markdown is too humble, the file organization is not beautiful, a website alone and some too. At the end, take a compromise and make a simple page with GitHub pages, and just do it. Eventually the wh

Overview Cost Function and BackPropagation Cost Function BackPropagation algorithm BackPropagation Intuition Back propagation in practice Implementation Note:unrolling Parameters Gradient Check Random initialization Put It together Application of Neural Networks Autonomous Driving Review Log 2/10/2017:all the videos; Puzzled about Bac

Statement:Machine learning series mainly records their own learning machine learning algorithms in the process of some references and summaries, including some of the content is reference books and reference blog.Directory: What are association rules The concept

This article describes the python Machine Learning Decision tree in detail (demo-trees, DTs) is an unsupervised learning method for classification and regression. Advantages: low computing complexity, easy to understand output results, insensitive to missing median values, and the ability to process irrelevant feature dataDisadvantage: the problem of over-matchi

---restore content starts---Matrix method in Machine learning 01: Linear system and least squaresDescription: Matrix Methods in Data Mining and Pattern recognition reading notesVery nice matrix online calculator, URL: http://www.bluebit.gr/matrix-calculator/.1. LU decompositionSuppose you want to solve a linear system now:Ax = B,Where A is a nxn non-singular square, there is a unique solution for any vector

. We need to introduce bagging method, bagging is bootstrap aggregative meaning, bootstraping idea is to rely on your own resources. Called self-help method, it is a kind of sampling method which has put back; metaphor is not necessary for outside help. Just rely on their own strength to become better--pull up by your own bootstraps! The following bagging policy:watermark/2/text/ahr0cdovl2jsb2cuy3nkbi5uzxqvthu1otcymdm5mzm=/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/ Dissolve/70/gravity/center

This two days to turn over the machine learning the actual combat this book, the algorithm is good, but the code is not friendly, the author is a algorithm, this from the code can be seen. But some places use NumPy array, make matrix, always feel strange, one is need to use three-way package numpy, although this package basic can say must, but for some novice, even pip are used not good, installed NumPy is

Original address: http://www.mimno.org/articles/ml-learn/Written by David MimnoOne of my students recently asked me for advice on learning ML. Here's what I wrote. It ' s biased toward my own experience, but should generalize.My Current Favorite Introduction is Kevin Murphy's book (Machine learning). Might also want to look at

0.0 The third is still mathematics, because mathematics is the basis for solving all problems, a question in depth to the last is the support of mathematical knowledge. The so-called basic decision superstructure, such as participation in the ACM competition, the game between the master is not programming skills, more is the mathematical knowledge of the competition. If you want to go far, the mathematical foundation must be played well. Well, it is a pity to learn mathematics before the exam, a

++ = 1.0 currline = line. strip (). split ('\ t') linearr = [] For I in range (21): linearr. append (float (currline [I]) If int (classifyvector (Array (linearr), trainweights ))! = Int (currline [21]): errorcount + = 1 errorrate = (float (errorcount)/numtestvec) print 'the error rate of this test is: % F' % errorrate return errorratedef multitest (): numtests = 10; errorsum = 0.0 for K in range (numtests): errorsum + = colictest () print 'after % d iterations the average error rate is: % F' %

) //Decision Tree ParametersVal numclasses = 5Val Categoricalfeaturesinfo=Map[int, Int] () Val impurity= "Gini"Val maxDepth= 5Val maxbins= 32//Build a decision tree model and trainVal model =Decisiontree.trainclassifier (Trainrdd, numclasses, Categoricalfeaturesinfo, impurity, maxDepth, MaxBins) //Test the test sampleVal Predictionandlabel = Testrdd.map {point = =Val Score=model.predict (Point.features) (Score, Point.label, Point.features)} Val showpredict= Predictionandlabel.take (50) printl

Download: https://pan.baidu.com/s/1Oeho172yfw1J6mCiXozQigTensorflow Machine Learning Practice Guide (Chinese Version pdf + English version PDF + Source Code)High-Definition Chinese PDF, 292 pages, with bookmarks, text can be copied and pasted;High Definition English PDF, 330 pages, with bookmarks, text can be copied and pasted;The Chinese and English versions can be compared.Supporting source code;Classic

After being confused by Hot Spot's messy and changing parameters, I decided to change things for fun. Then we found the machine learning video on Coursera. Reading a few paragraphs is quite simple, so I recorded them in itouch and checked them out from time to time. The day before yesterday, I finally finished eating it. The content is really easy to understand. Now regression,NeuralNetworkK means I can als

the time to report an error: Exception in thread "main" Java.lang.VerifyError:class Jdk.nashorn.internal.objects.ScriptFunctionImpl Overrides final method Setprototype. This I can not know what is the problem, to the current level can not be the same as before the wrong, the class file is also generated, the inside of what is not known (perhaps with the high version of the JDK8 to compile OpenJDK8 has a relationship), toss long enough, the first compile JDK source code, JDK Compilation is a suc

learning_rate.2. The parameters of the tree are tuned around the initialization, and the order of the tuning is Max_depth, Min_samples_split, Min_samples_leaf, Max_features, and the visual computing ability is optimal or the combination parameter is tuned.3. Determine the optimal combination of parameters, and then reduce the learning_rate, increasing the n_estimators of the same multiples, until the computational power reaches the limit or the model on the validation set is very small. Origina

criteria for the end of recursion are:1: All class tags are exactly the same, return the class label (this is not nonsense, all the same, the class of the hair)2: Using all the groupings or not dividing the dataset into groups that contain only unique categories, since we cannot return a unique one, then we are represented by a wave. Is our majority voting mechanism above, returning the category with the most occurrences. This is not the NPC,.The code is as follows:People can not understand the

Sample=cutstring (U) It is learnt that the car is nicknamed the Beast and the Beast is likely to be used in January 2017 when the 45th President of the United States took office. At present, the detailed specifications of the beast are classified information, but spy photos show the Beast adopted the Cadillac's latest grille and headlight design. ") tokenstr=nltk.word_tokenize (sample) FDIST3=NLTK. Freqdist (tokenstr) print "---the number of U.S. occu

Kmenas algorithm is relatively simple, not detailed introduction, directly on the code.Importorg.apache.log4j. {level, Logger}ImportOrg.apache.spark. {sparkconf, sparkcontext}Importorg.apache.spark.mllib.linalg.VectorsImportorg.apache.spark.mllib.clustering._/*** Created by Administrator on 2017/7/11. */Object Kmenas {def main (args:array[string]): Unit={ //setting up the operating environmentVal conf =NewSparkconf (). Setappname ("Kmeans Test"). S

-0.576525 11.778922 0-0.346811-1.678730 1-2.124484 2.672471 11.217916 9.597015 0-0.733928 9.098687 0-3.642001-1.618087 10.315985 3.523953 11.416614 9.619232 0-0.386323 3.989286 10.556921 8.294984 11.224863 11.587360 0-1.347803-2.406051 11.196604 4.951851 10.275221 9.543647 00.470575 9.332488 0-1.889567 9.542662 0-1.527893 12.150579 0-1.185247 11.309318 0-0.445678 3.297303 11.042222 6.105155 1-0.618787 10.320986 01.152083 0.548467 10.828534 2.676045 1-1.237728 10.549033 0-0.683565-2.166125 10.229