udemy data science and machine learning with python

Reprint: http://blog.csdn.net/u012162613/article/details/44261657 This article is part of the third chapter of the overview of neural networks and deep learning, which is a common regularization method in machine learning/depth learning algorithms. (This article will continue to add) regularization method: Prevent ove

To facilitate the understanding of the later learning, record!Run-time Data area1. Thread Sharing1.1 Method Area1.1.1 Running a constant-volume pool (runtime Constant)1.2 Heaps (heap)2. Thread-Private2.1 Virtual machine stack (VM stack)2.2 Local method Stack (Native)2.3 Procedure Counter (program Counter Register)3. Direct MemoryVirtual

hypothesis could not being built up,Generlly the number of hypothesisThat can is built is less than a^b.Let's come back to the inequlity, we can prove it mathematically thatif M can be replaced by a polynomial, which means the number of hypothesis in a set are not infinite and then we can declar E that learning was feasible using this hypothesis set.There is a new statement this wil be proved next lecture, if the maxnum of hypothesis are less than it

Algorithms have become an important part of our daily lives, and they almost appear in any area of business. Gartner, the research firm, says the phenomenon is "algorithmic commerce", where algorithmic commerce is changing the way we operate and manage companies. Now you can buy these various algorithms for each business area on the "algorithmic market". The algorithmic market provides developers with more than 800 algorithms, including sound and visual processing,

1. Background When I was outside the company internship, a great God told me that learning computer is to a Bayesian formula applied to apply. Well, it's finally used. Naive Bayesian classifier is said to be a lot of anti-Vice software used in the algorithm, Bayesian formula is also relatively simple, the university to do probability problems often used. The core idea is to find out the most likely effect of the eigenvalue on the result. The formula

[:, 1].max () + 1, 0.005 +grid_x =Np.meshgrid (Np.arange (L, R, h), A Np.arange (b, T, v)) atflat_x = Np.c_[grid_x[0].ravel (), grid_x[1].ravel ()] -Flat_y =model.predict (flat_x) -Grid_y =Flat_y.reshape (Grid_x[0].shape) -Mp.figure ('Logistic Classification', -Facecolor='Lightgray') -Mp.title ('Logistic Classification', fontsize=20) inMp.xlabel ('x', fontsize=14) -Mp.ylabel ('y', fontsize=14) toMp.tick_params (labelsize=10) +Mp.pcolormesh (Grid_x[0], grid_x[1], grid_y, cmap='Gray') -Mp.scatter

http://blog.csdn.net/ppn029012/article/details/8908104 Machine Learning---2. From maximum likelihood to view linear regression classification: Mathematics machine Study 2013-05-10 00:34 3672 people read comments (15) Collection Report MLE machine learning Directory (?) [+]

1. Background Decision Book algorithm is a kind of classification algorithm approximating discrete numbers, which is simpler and more accurate. International authoritative academic organization, Data Mining International conference ICDM (the IEEE International Conference on Data Mining) in December 2006, selected the ten classical algorithms in the field of mining, C4.5 algorithm ranked first. C4.5 algorit

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice fo

Maximumlikelihood (p=w): H,t=defineparam () f1=factorial (h+t)/(factorial (H) *factorial (T)) f2= (p**h) * ((1.0-p) **t) return F1*F2 def factorial (x): return reduce (lambda x,y:x*y,range (1,x+1)) achieve the effect, corresponding to the above example, when h=49,t=31, is the probability of P=2/3 probabilitiesCode Address: Please click on my/********************************* This article from the blog "Bo Li Garvin"* Reprint Please indicate the sourc

Full Stack Engineer Development Manual (author: Shangpeng) Python Tutorial Full Solution Keras uses a depth network to achieve the encoding, that is, the n-dimensional characteristics of each sample, using K as a feature to achieve the function of coding compression. The feature selection function is also realized. For example, the handwriting contains 754 pixels, and it contains 754 features, if you want to represent them with two features. How do yo

The idea of clustering: dividing a DataSet into several subsets (called a cluster cluster) that you don't want to cross, each potentially corresponding to a concept. But the practical significance of each cluster is determined by the users themselves, and the clustering algorithm will only be divided.The role of Clustering:1) can be used as a separate process for finding a distribution pattern of data2) as a preprocessing process for classification. First, classify

-za-z]"," ", Sent.lower (). Strip ()). Split () in sentences.append (temp) - to returnsentences + - #The sentences in the long news are stripped out for training . thesentences = [] * forIinchx: $Sentence_list =news_to_sentences (i)Panax NotoginsengSentences + =sentence_list - the + #Configure the dimension of the word vector ANum_features = 300 the #the frequency of the words that are to be considered +Min_word_count = 20 - #number of CPU cores used in parallel computing $Num_workers =

Python Kmeans clustering is relatively simple, first requires the import NumPy, from the Sklearn.cluster import Kmeans module:Import NumPy as NP from Import KmeansThen read the TXT file, get the corresponding data and convert it to numpy array:X == open ('rktj4.txt') for in f: = Re.compile ('\s+') x.append ([Float (Regex.Split (v) [3]), float ( Regex.Split (v) [6= Np.array (X)Set the number of classe

are two issues to note:1, if the data is linearly non-divided.When the data is linearly non-divided, we can also use the above method, but will come to an unacceptable solution, at this time we can detect whether the solution is valid to determine whether our data can be divided.2. What happens if W0 exists in Z?In our previous assumptions, W0 represents a const

("Training"); Trainingdata.foreach (function(item) {classifier.adddocument (Item.text, Item.label); }); varStartTime =NewDate (); Classifier.train (); varEndTime =NewDate (); varTrainingtime = (endtime-starttime)/1000.0; Console.log ("Training Time:", Trainingtime, "seconds"); Loadtestdata ();}functionLoadtestdata () {Console.log ("Loading test Data"); Fs.readfile (' Test_data.json ', ' utf-8 ',function(err,

sessions should be conducted before they can be completed?In general, the number of sessions = total size of the sample/out-of-sample data. SizeHow many data should you choose to use as an out-of-sample data?The different requirements have different options, but one rule of thumb is:Out-of-sample data size = Total siz

In the model training, especially in the training set to do cross-validation, usually want to save the model, and then put on a separate test set test, the following is the Python training model to save and reuse.Scikit-learn already has the model persisted operation, the import joblib canfromimport joblibModel Save>>> Os.chdir ( "Workspace/model_save" ) >>> from sklearn import SVM >>> X = [[0 , 0 ], [1 , 1 ]]>>> y = [ 0 , 1 ]>>> CLF = SVM. SV

Python code implementation on the perception machine ----- Statistical Learning Method Reference: http://shpshao.blog.51cto.com/1931202/1119113 1 #! /Usr/bin/ENV Python 2 #-*-coding: UTF-8-*-3 #4 # Untitled. PY 5 #6 # copyright 2013 T-dofan There are still a few questions, the book's adjustment strategy is: Wi = wi

Before installing Scikit-learn, you need to install numpy,scipy. However, there are always errors when installing scipy (pip install scipy). After a series of lookups, the reason is that scipy relies on numpy and many other libraries (such as Lapack/blas), but these libraries are not easily accessible under Windows.After finding, the discovery can be solved by another way, http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpyDownload here: Numpy-1.11.2+mkl-cp34-cp34m-win32.whl Scipy-0.18.1-c