プログラミング

【ImageDataGenerator】Data Augmentation Of Training Images In Keras

This article describes how to use keras' ImageDataGenerator to extend data.
2022.04.04

【AI Platform Notebooks】Using Jupyter With GCP

I will describe how to use Jupyter lab in AI Platfrom Notebooks and how to add a new library in Anaconda.
2022.04.04
プログラミング

【Python】Creating a wireframe in Plotly.

This article is a reminder to create a wireframe in plotly. In plotly, I could not find a function to visualize wireframes like matplotlib's plot_wireframe(), so I created one.
2022.04.04

Theory And Implementation Of Multiple Regression Analysis

This article covers multiple regression analysis to model the relationship between multiple explanatory and objective variables and the derivation of a normal equation for the regression coefficient. It will also include a full-scratch Python and scikit-learn implementation.
2022.04.04
備忘録

【Python, pandas】Delete All/Any 0 Columns And Rows

This article is a reminder of the following information regarding pandas. How to delete all 0 columnsHow to delete e...
2022.04.03
機械学習

Theory And Implementation Of Least Squares Method

We will introduce the theory of linear regression, which has a long history in machine learning, especially the least-squares method, and its implementation using python and scikit-learn.
2022.04.03
備忘録

【Python】About the argument key when sorting

Describes sorting in python, using lambda for keys and sorting multiple keys.
2022.04.02
数学

Method Of Lagrange Multiplier Under Equality Conditions

This article describes Method Of Lagrange Multiplier, a powerful technique for solving mathematical optimization problems.
2022.04.02
機械学習

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

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
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