data scientist vs machine learning engineer

Data Set Classification in machine learning with supervised (supervise), datasets are often divided into two or three groups: the training set (train set) validation set (validation set) test set. The training set is used to estimate the model, the validation set is used to determine the network structure or the parameters that control the complexity of the mod

neural network are in the same form.2, for the RBF network the first level input parameters are fixed: | | x-μi| |, but for neural network, the corresponding parameters need to be learned by reverse propagation.3, for the RBF network when the first level input value is very large, the corresponding node output will become very small (Gaussian model), and for the neural network does not exist this feature, the root of the specific node used by the function. 4. RBF and Kernel methodsThen look at

, 0], [3, 3, 4, 0, 0, 0, 0, 2, 2, 0, 0], [5, 4, 5, 0, 0, 0, 0, 5, 5, 0, 0], [0, 0, 0, 0, 5, 0, 1, 0, 0, 5, 0], [4, 3, 4, 0, 0, 0, 0, 5, 5, 0, 1], [0 , 0, 0, 4, 0, 4, 0, 0, 0, 0, 4], [0, 0, 0, 2, 0, 2, 5, 0, 0, 1, 2], [0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0], [1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]]) >>> Svdrec.recommend (Mymat,1,estmethod=svdrec.svdest) the 0 and 3 similarity is:0.490950the 0 and 5 similarity is : 0.484274the 0 and Similarity is:0.512755the 1 and 3 similarity is:0.491294the 1 and 5 si

data.X = [[1.,-1., 2.], [2., 0., 0.], [0.,1.,-1.]] Binarizer= preprocessing. Binarizer (). Fit (X)#The default threshold value is 0.0PrintBinarizer#Binarizer (copy=true, threshold=0.0)Printbinarizer.transform (X)#[1.0. 1.]#[1.0. 0.]#[0.1. 0.]Binarizer= preprocessing. Binarizer (threshold=1.1)#set the threshold value to 1.1Printbinarizer.transform (X)#[0.0. 1.]#[1.0. 0.]#[0.0. 0.]4. Label preprocessing (label preprocessing)4.1) Label binary value (label binarization)Labelbinarizer is typica

attribute in the data set. The general situation is somewhere between the two.D. High-dimensional mappingMap properties to high-dimensional space. This is the most precise approach, which completely retains all the information and does not add any additional information. For example, Google, Baidu's CTR Prediction model, pre-processing will be all the variables to deal with this, up to hundreds of millions of dimensions. The benefit of this is that t

Stitching raw DATA:Train_data = pd.read_csv (' train.csv ') Test_data = pd.read_csv (' test.csv ') All_data = Np.vstack ((train_data.ix[:,1:-1], TEST_DATA.IX[:,1:-1]))Merge array Vstack and Hstack functions under NumPy:>>> a = Np.ones ((2,2)) >>> B = Np.eye (2) >>> print Np.vstack ((A, b)) [[1. 1.] [1. 1.] [1. 0.] [0. 1.]]>>> Print Np.hstack ((A, b)) [[1. 1. 1. 0.] [1. 1. 0. 1.]Generate a high (2) secondary feature:def group_data (data, degr

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} \rceil\), between the random gradient descent method and the batch gradient descent methodThe low-volume gradient descent method is faster than the random gradient descent method because the frequency of the update \ (\theta\) is faster than the random gradient descent method because it is possible to accelerate the quantization operation when the differential is computed (that is, matrix multiplication).Third, verifying the convergence of the cost functionCalculate \ (Cost (\theta, (x^{(i)},

Do data science generally need to use similar xgboost, tensorflow, such as libraries, these libraries in win is not so good installation, but many people need them, how to do it, the simplest is to use Docker, not only a Linux virtual environment, You can also use Windows at the same time. It is actually a fairly easy to use software, this article does not teach too many commands, because I will not, will only speak a few basic commands. This article

Preface: This article describes Ng's notes about machine learning about SVM. I have also learned some SVM theories and used libsvm before. However, this time I have learned a lot about Ng's content, and I can vaguely see the process from Logistic model to SVM model. Basic Content: When using the linear model for classification, You can regard the parameter vector as a variable. If the cost function

, the choice of the first variable, in the SMO algorithm overview I also introduced, is the least satisfied with the kkt condition of this problem, kkt conditions are as follows (Kkt is relative to each sample point is (Xi,yi)):　　　　　　　　　　　　　　　　　　G (xi) if the above-mentioned formulaThe selection of the first variable is the outer loop of the SMO, and in the process of inspection, the first step is to traverse all the sample points that satisfy the 0(2) followed by the choice of the second variab

basis of Common machine learning Data mining knowledge points SSE (Sum of squared error, squared error and) SSE=∑I=1N (Xi−x⎯⎯⎯) 2 sse=\sum_{i=1}^{n} (x_i-\overline{x}) ^2 SAE (sum of Absolute error, absolute error and) sae=∑i=1n| xi−x⎯⎯⎯| sae=\sum_{i=1}^{n}| x_i-\overline{x}| SRE (Sum of Relative error, relative error and) Sre=∑i=1nxi−x⎯⎯⎯x⎯⎯⎯sre=\sum_{i=1}^{n}{

Hinton, one of the deep learning giants, has a classic paper visualizing data using T-sne in the field of dimensionality reduction. This method is the classic of the manifold (non-linear) data dimensionality reduction, and there are few new dimensionality reduction methods to be completely surpassed. Compared with PCA and other linear methods, this method can eff

Summary:Orange Orange is a component-based data mining and machine learning software suite that features a friendly, yet powerful, fast and versatile visual programming front end for browsing data analysis and visualization, and the base binds Python for scripting development. It packs Orange Orange is a component-bas

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

IntroducedCan a machine tell the variety of flowers according to the photograph? In the machine learning angle, this is actually a classification problem, that is, the machine according to different varieties of flowers of the data to learn, so that it can be unmarked test i

Linear regressionPros : Results are easy to understand and computationally uncomplicatedcons : Poor fitting of non-linear dataapplicable data type : numeric and nominal type dataThe goal of regression is to predict the target value of the numerical type. The most straightforward approach is to write a calculation formula for the target value based on the input. This formula is the so-called regression equation (regression equation), wherein the parame

Weight vector W, training sample X1. Initialize the weight vector to 0, or initialize each component to any decimal between [0,1]2. Input the training sample into the Perceptron to get the classification result (-1 or 1)3. Update weight vectors based on classification resultsPerceptron algorithm for Tuyi data samples that are linearly delimitedMachine learning--perceptron

Prediction problems in machine learning are usually divided into 2 categories: regression and classification .Simply put, regression is a predictive value, and classification is a label that classifies data.This article describes how to use Python for basic data fitting, and how to analyze the error of fitting results.This example uses a 2-time function with a ra