best linear algebra book for machine learning

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The linear regression of Python machine learning

=linearr.predict (X_train) #基于训练集得到的线性y值Plt.figure ()Plt.scatter (x_train,y_train,color= ' green ') #原始训练集数据散点图Plt.plot (x_train,y_train_pred,color= ' black ', linewidth=4) #线性回归的拟合线Plt.title (' Train ') #标题Plt.show ()Y_test_pred=linearr.predict (X_test)Plt.scatter (x_test,y_test,color= ' green ') #绘制测试集数据散点图Plt.plot (x_test,y_test_pred,color= ' black ', linewidth=4) #基于线性回归的预测线Plt.title (' Test ')Plt.show ()Print (' mse= ', Sm.mean_squared_error (y_test,y_test_pred)) #MSE值Print (' r2= ', Sm.r2_

Linear regression ii__ algorithm and machine learning for regression problems

1. Linear regression (linear regression): B, multivariate linear regressionMultivariate linear regression: The form is as follows: The order is therefore: there are parameters: Then, the cost function (the price functions) is: Note: N:number of features (total number of features) M:number of training examples (number

Open Course Notes for Stanford Machine Learning (I)-linear regression with single variables

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Model Representation ( Model Creation ) Consider a question: what if we want to predict the price of a house in a given area based on the house price and area data? In fact, this is a linear regression problem. The given data is used as a training sample to train it to get a model that represents the relationship between price and area (actually a functi

Machine Learning-ii. Linear Regression with one Variable (Week 1)

http://blog.csdn.net/pipisorry/article/details/43115525Machine learning machines Learning-andrew NG Courses Study notesSingle-Variable linear regression linear regression with one variableModels represent model representationExample:This is regression problem (one of the supervised

Machine learning dimensionality reduction algorithm two: LDA (Linear discriminant analysis)

are two classes, then we want them to be able to project to the Axis 1 (the PCA result is axis 2), so in one-dimensional space is also very easy to distinguish. Next is the derivation, because here the formula is very inconvenient, I quoted Deng Cai Teacher of a PPT in a small section of the picture: The idea is still very clear, the objective function is the last line J (a), μ (a float) is the map of the center to evaluate the distance between classes, S (one scoop) is the map of the point

Machine Learning Practice: implementation of Single-Variable Linear Regression

: length (theta1_vals) t = [theta0_vals (I); theta1_vals (j)]; j_vals (I, j) = computecost (X, Y, t ); endend (5) figure; % create a graph (6) contour (theta0_vals, theta1_vals, j_vals, logspace (-2, 3, 20); % draw a contour map (7) xlabel ('\ theta_0'); ylabel ('\ theta_1'); if we want to draw the theta0 and theta1 results of linear regression on the contour map, we can: plot (theta (1), theta (2), 'rx ', 'markersize', 10, 'linewidth', 2 ); 4. Draw

Machine learning Techniques (1)--linear support Vector machines

solved?Use quadratic programming to solve! Additional knowledge: https://en.wikipedia.org/wiki/Quadratic_programmingQuadratic programming (QP) is a special type of mathematical optimization problem--specifically, the problem of optimizing (either minimzing or maximizing) a quadratic function of several variables subject to linear constraints on these variabl Es.Problem formulation:The quadratic programming problem with n variables and m constra

Machine learning notes-exponential distribution clusters and generalized linear models

So far, we've talked about the regression and classification examples, in the regression example:In the classification example:As you can see, μ and Φ are defined as functions of x and θ.As we'll see in this article, these two models are actually just a special case of a broad model family, generalized linear models. We will also demonstrate how the other models of the generalized linear model family are de

Machine learning basics: linear regression and Normal Equation

This article will cover: (1) Another Linear Regression Method: normal equation; (2) Advantages and Disadvantages of gradient descent and normal equation; Previously we used the Gradient Descent Method for linear regression, but gradient descent has the following features: (1) learning rate needs to be selected in advance; (2) Multiple iteration is required; (

Machine Learning-week 4-non-linear Hypotheses

Why is computer image recognition difficult? Because we see the car, and the computer sees the RGB value that represents the color. The computer needs to be judged by these values.If the picture is 50 * 50 pixels, then a total of 2,500 pixels. If it is quadratic features, then XI, XJ's combination has 2500 + 2499 + ... + 1 about 300 million.Neurons and the BrainThe brain can learn many algorithms, but the program is fixed (birth to death no one changes your brain program). This kind of

Life is too short to learn PYTHON50 books (including Basics, algorithms, machine learning, modules, crawler frames, Raspberry Pi, etc.) there's always a book you want.

and is easily downloaded and modified by the reader.The following books will not be introduced, share the graphic coverHere is still to recommend my own built Python development Learning Group: 725479218, the group is the development of Python, if you are learning Python, small series welcome you to join, everyone is the software Development Party, not regularly share dry goods (only Python software develo

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