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Regularization theory mainly used in the branch of mathematics and in particular in the fields of machine learning and inverse problems. This concept used in order to solve an ill-posed inverse problem or to prevent overfitting. This information is usually of the form of a penalty for complexity, such as restrictions for smoothness or bounds on the vector space norm. Conversion of machine learning problems to ill-posed inverse and how we can apply these techniques in real life problem should be learned. This books gives little idea to do the above job.