best linear algebra book for machine learning

-variable linear regression model.The process of solving this problem with the machine learning method is actually using the training algorithm to process the data in the training set, get our regression equation h, and then with the new data, we can use the regression equation h to calculate the value of the corresponding output y when we only know the input x.

NormalizationBy looking at the values, note this House sizes is about the number of bedrooms. When features differ by orders of magnitude, first performing feature scaling can make gradient descent converge much more QuicklyThat is, when there is a large difference between features, such as the size of the house and the number of bedrooms, this will cause the gradient descent convergence is relatively slow, as shown in (left) , when the characteristics are normal, gradient descent convergence f

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ensure reversible ( reversible Sufficient condition : matrix X columns linearly independent )In retrospect, our approach is to use iterative methods to find out the value of the cost function, and not to find the cost function. That is to say, whether the so-called optimal solution can be obtained, either by iteration or by other means, in line with the above conditions.But the reality of the data is not so ideal.If not reversible, how to solve?1, to seek pseudo-inverse (statistics solution )2.

Machine learning notes (b) univariate linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. Model representationHow to solve the problem of house price in note (a), this will be

0.0267 0 11 0.245 0.057 0 12 0.343 0.099 0 13 0.639 0.161 0 14 0.657 0.198 0 15 0.36 0.37 0 16 0.593 0.042 0 17 0.719 0.103 0 Logistic regression:refer to the "machine learning combat" co

of finding the best fitting line is actually looking for the best b b and M M. In order to find the best fit line, here we first define what line is the best line. We define error (cost function): Error function errors (b,m) =1n∑1n ((B+MXI) −yi) 2 error functions \ error_{(b, M)}=\frac{1}{n}\sum_{1}^{n} ((b+mx_i)-y_i) ^{2} The Python code that calculates the loss function is as follows: # y = b + mx def compute_error_for_line_given_points (b, M, points): totalerror = SUM ((((b + M * point[

　　From a statistical point of view, most of the methods of machine learning are statistical classification and regression method to the field of engineering extension.The term "regression" (Regression) was the origin of the British scientist Francis Galton (1822-1911) in a 1886 paper [1] to study the relationship between height and parental height of a child. After observing 1087 couples, the adult son was

build the model.In the exponential distribution family expression of the Bernoulli distribution we have known:, thus obtained.Three assumptions for building a generalized linear model: Assuming that the Bernoulli distribution is met, , in Bernoulli distribution The derivation process is as follows:As with the least squares model, the next work is done by gradient descent or Newton's method.Note the above push to the result, rec

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

based on the minimum mean variance. The closer to the predicted point, the heavier the weight, which is to use the points near the check to give higher weights. The most common is the Gaussian nucleus. The weights corresponding to the Gaussian nuclei are as follows:In (Formula 2), the only thing we need to make sure is that it's a user-specified parameter that determines how much weight is given to nearby points.Therefore, as shown in (Equation 3), local weighted