From cd33976fcb61fd6968590f5ea86faea75c5101ea Mon Sep 17 00:00:00 2001 From: mhjensen Date: Sat, 14 Sep 2019 19:15:18 +0200 Subject: [PATCH] correcting typos in log reg --- doc/src/LogisticRegression/LogReg.do.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/src/LogisticRegression/LogReg.do.txt b/doc/src/LogisticRegression/LogReg.do.txt index 67812e97c..1966fb182 100644 --- a/doc/src/LogisticRegression/LogReg.do.txt +++ b/doc/src/LogisticRegression/LogReg.do.txt @@ -11,7 +11,7 @@ coefficients of a functional fit (say a polynomial) in order to be able to predict the response of a continuous variable on some unseen data. The fit to the continuous variable $y_i$ is based on some independent variables $\hat{x}_i$. Linear regression resulted in -analytical expressions (in terms of matrices to invert) for several +analytical expressions for standard ordinary Least Squares or Ridge regression (in terms of matrices to invert) for several quantities, ranging from the variance and thereby the confidence intervals of the parameters $\hat{\beta}$ to the mean squared error. If we can invert the product of the design matrices, linear @@ -40,7 +40,7 @@ failure etc. Logistic regression will also serve as our stepping stone towards neural network algorithms and supervised deep learning. For logistic learning, the minimization of the cost function leads to a non-linear -equation in the parameters $\hat{\beta}$. The optmization of the problem calls therefore for minimization algorithms. This forms the bottle neck of all machine learning algorithms, namely how to find reliable minima of a multi-variable function. This leads us to the family of gradient descent methods. The latter are the working horses of basically all modern machine learning algorithms. +equation in the parameters $\hat{\beta}$. The optimization of the problem calls therefore for minimization algorithms. This forms the bottle neck of all machine learning algorithms, namely how to find reliable minima of a multi-variable function. This leads us to the family of gradient descent methods. The latter are the working horses of basically all modern machine learning algorithms. We note also that many of the topics discussed here regression are also commonly used in modern supervised Deep Learning