machine learning with tensorflow book

ready-made algorithm and slightly modifying it may not be the best choice. Data scientists should still learn the most important algorithms, how to develop them, and how to choose the most appropriate algorithms based on their intentions? "think Big data" 's infographic shows 12 of the most important algorithms for different applications, and presumably this is something that every data scientist would have loved.Note: The Chinese translation in the Chinese information map is a reference to the

finite but large quantities of t instead; second, using the bootstrapping method in statistics To generate new data based on existing data simulations.bootstrappingThe data sampled by Bootstrap is randomly averaged out in the original n data, recorded and then re-extracted, and then taken n times, the resulting data is statistically referred to as Bootstrap sample.BaggingThe method of bootstrap aggregation (BAGging) is to generate a series of different bootstapping mechanisms, which are gt then

This paper mainly records the contents of the second chapter in "Machine Learning in Action". The book introduces KNN (k nearest neighbors) with two specific examples, namely: Date Object Predictions Handwritten digit recognition With the "Date Object" function, it is basic to understand how the KNN algorithm works. Handwritten numeral recogniti

Recently turned Peter Harrington "machine Learning Combat", see the Logistic regression chapter a little bit of doubt.After a brief introduction of the principle of logistic regression, the author immediately gives the code of the gradient rise algorithm: The range of the algorithm to the jump is a bit large, the author himself said, here omitted a simple mathematical deduction.So in fact, this process is a

Prepare for machine learning using Python The machine learning getting started book "Machine Learning Practice" uses the python language. The following describes how to use Python to pr

Machine learning goals: Let machines learn to complete tasks through several instances. Statistics is a field that machine learning experts often study. The machine learning method is not a waterfall process. It needs to be analyz

Four machine learning dimensionality reduction algorithms: PCA, LDA, LLE, Laplacian eigenmapsIn the field of machine learning, the so-called dimensionality reduction refers to the mapping of data points in the original high-dimensional space to the low-dimensional space. The essence of dimensionality is to learn a mapp

Recently reviewed the machine learning knowledge, here to summarize, there are many online, most of them are not full or wrong. The following is mainly to see me briefly summarize the knowledge points of regression analysis.1. Summary of Contents(1) Linear regression(2) Logistic regression (logistic)(3) Maximum likelihood estimation(4) Gradient descent2. Linear regression(1) We used to learn the linear func

parameter, which defaults to 1.0 and we set it to 0.01.nbc_6 = Pipeline([ (‘vect‘, TfidfVectorizer( stop_words=stop_words, token_pattern=ur"\b[a-z0-9_\-\.]+[a-z][a-z0-9_\-\.]+\b", )), (‘clf‘, MultinomialNB(alpha=0.015) [0.91073796 0.92532037 0.91604065 0.91294741 0.91202476]Mean score:0.915 (+/-0.003) This score has been optimized for the better.Evaluating classifier PerformanceWe have obtained better classifier parameters by cross-v

In machine learning practice, the code is implemented using python2. Some errors may occur when you use python3. These errors are caused by different syntaxes or incomplete code. The following changes are made during the learning process for your convenience. 1. iteritems () in page40, majoritycnt () (1) In python2.x, iteritems () is used to return the iterator a

sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0]This is very similar to the voting portion of the KNN algorithm.The next step is to create a decision tree code based on the above method: def createtree(dataset,labels):Classlist = [example[-1] forExampleinchDataSet]#当某一分支下所有数据的类型相同停止 ifClasslist.count (classlist[0] = = Len (classlist):returnclasslist[0]#当数据集中所有属性已经被划分完毕时结束, the two cases are combined here, regardle

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, ev

applied to the numerical attribute, for the ordinal attribute can be transformed to a numerical type, the nominal attribute normalization is also better, but the two-dollar attribute may not be very good. Main advantages and Disadvantages:Advantages: High accuracy, insensitive to noise, no data input assumptions requiredCons: High complexity of time and space, need to determine K value (k value determination may require a lot of experience)Here is the implementation of the KNN algorithm in the

Today read "Machine learning combat" read the use of the K-Near algorithm to improve the matching effect of dating sites, I understand, but see the code inside the data sample set DatingTestSet2.txt a little bit, this sample set where, only gave me a file name, no content ah.Internet Baidu This file name, found a lot of bloggers can download the blog, I am very curious, is also read "

method is to see how much better the predicted value is compared to using the mean only. The interval is usually between (0,1). 0 means that it is not as predictable as to take the mean directly, while 1 means that all predictions match the real results perfectly.The calculation method of R2, different literature is slightly different. As the function R2 in this paper is based on the Scikit-learn official website document, with the Clf.score function results consistent.The implementation of the

Absrtact: Recently in the "Machine learning actual Combat", in the process of code will always report some small errors, so the place of the debug; because it is jumping to see, so just a part of it, I hope that after the book I met all the errors are here to correct.Content:Nineth Chapter (regression tree): Mat0 = Dataset[nonzero (dataset[:,feature] >va

are 1+d, which is equivalent to the VC dimension of z space, so when Q becomes larger, the VC dimension becomes larger.Generalization problem (generalization Issue)We go back to machine learning is basically a balance between the compromise problem, if D (q), we can make ein very small, but this will lead to Ein and eout very different, when D (Q) small, can make Ein and eout difference small, but can not

Tools used: NumPy and MatplotlibNumPy is the most basic Python programming library in the book. In addition to providing some advanced mathematical algorithms, it also has a very efficient vector and matrix operations function. These are particularly important for computational tasks for machine learning. Because both the characteristics of the data, or the batch

See Original book section 1.5General process for building predictive modelsThe problem of the daily language expression--the problem of the mathematical language restatementRestatement of problems, extraction features, training algorithms, evaluation algorithmsFamiliar with the input data structure of the different algorithms:1. Features required to extract or combine predictions2. Set the training target3. Training model4. Evaluate the performance of

mistakenly classified data (x, y), there is-y (wx + B)> 0 (Buddha said: Too lazy to say ). Then there is a loss function (proving something to die ): 　　 Then the loss function is minimized (-_-zzz ): 　　 The perception machine learning algorithm is drive by mistake (the word "driven" sounds very powerful), and the Stochastic Gradient Descent Method (which will be written later ), evaluate the skewness for W