プログラミング

Support Vector Machine (SVM) Implementation In Python

Implement a hard-margin support vector machine with full scratch and scikit-learn. In this section, we will implement the machine using Python.
2022.04.03
機械学習

Theory of Support Vector Machines (SVM)

The theory of Hard Margin Support Vector Machines (SVMs) is explained in an easy-to-understand manner.SVMs are a type of supervised machine learning algorithm for pattern identification. It is an excellent two-class classification algorithm with the idea of "maximizing margins."
2022.04.03
数学

The Distance Between A Point And A Hyperplane

Derive the distance between a point and a hyperplane, which is the general form of the distance between a point and a line.
2022.04.01
プログラミング

【Python】Implementation Of Logistic Regression

Logistic regression is implemented using full scratch and scikit-learn. In this section, we will implement it using Python.
2022.04.05
数学

Derivation Of “Differentiate By Vector” Formula

When studying machine learning theory, we often see the operation of "differentiating a scalar by a vector. In this article, we derive the formula for "differentiating a scalar by a vector.
2022.04.01
機械学習

Explanation Of Logistic Regression Theory

The theory of logistic regression is explained in simple terms. Estimating the value of a parameter based on n pairs of observed data yields a model that outputs the probability of a response to any given numerical level.
2022.04.03
プログラミング

Principal Component Analysis (PCA), Python Code

Principal Component Analysis (PCA) is implemented using full-scratch and scikit-learn. In this section, we will implement it using Python.
2022.04.03
機械学習

Principal Component Analysis (PCA) Theory

The theory of principal component analysis (PCA), a method of dimensional compression, is explained. Principal Component Analysis is a method to summarize the information of multidimensional data observed on mutually correlated features into new features expressed as a linear combination of the original features without losing any information as much as possible.
2022.04.03
確率・統計学

Transformation Of Random Variables

Consider the transformation of a random variable X into Y = g(X) to derive the form of the transformed probability density function.
2022.04.01
数学

Binomial And Polynomial Theorem Proofs

The binomial theorem is a very versatile theorem that shows up in a variety of places. In this article, we will discuss the binomial theorem and its generalization, the polynomial theorem.
2022.04.01
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