By Akhil Wali
Clojure for computing device studying is an advent to computing device studying strategies and algorithms. This ebook demonstrates how one can observe those suggestions to real-world difficulties utilizing the Clojure programming language.
It explores many computer studying innovations and likewise describes the best way to use Clojure to construct desktop studying structures. This publication begins via introducing the straightforward laptop studying difficulties of regression and type. It additionally describes how one can enforce those desktop studying recommendations in Clojure. The e-book additionally demonstrates numerous Clojure libraries, that are valuable in fixing computer studying problems.
Clojure for computer studying familiarizes you with a number of pragmatic computing device studying ideas. by way of the tip of this ebook, you can be absolutely conscious of the Clojure libraries that may be used to resolve a given computer studying challenge.
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Extra resources for Clojure for Machine Learning
We first get the value of the :fitted key from the returned map and add it to the scatter plot using the add-lines function; this is shown in the following code: (def samp-linear-model (linear-model Y X)) [ 44 ] Chapter 2 (defn plot-model  (view (add-lines samp-scatter-plot X (:fitted linear-samp-scatter)))) (plot-model) This code produces the following self-explanatory plot of the linear model over the scatter plot we defined previously: The previous plot depicts the linear model samp-linear-model as a straight line drawn over the 10 data points that we defined in X and Y.
This function generates a matrix of normally distributed random elements with optionally specified mean and standard deviations. The matrix is normally distributed in the sense that all the elements are distributed evenly around the specified mean value with a spread specified by the standard deviation. Thus, a low standard deviation produces a set of values that are almost equal to the mean. The cl/rnorm function has several overloads. 20e+01 . 05e+01 . 78e+01 . 48e+00 ... 62e+01 . 69e+01 In the preceding example, the first call specifies the mean, the standard deviation, and the number of rows and columns.
5862250345284887 [ 47 ] Understanding Linear Regression The mean-squared error (MSE) measures the average magnitude of errors in a formulated model without considering the direction of the errors. We can calculate this value by squaring the differences of all the given values of the dependent variable and their corresponding predicted values on the formulated linear model, and calculating the mean of these squared errors. The MSE is also termed as the mean-squared prediction error of a model. If the MSE of a formulated model is zero, then we can say that the model fits the given data perfectly.