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# Yukkuri Machine Learning

機械学習の理論、実装について勉強したことを分かりやすく書くブログです。数学やプログラミング、たまに物理学の内容も書いています。

## Sitemaps

- [XML Sitemap](https://laid-back-scientist.com/sitemap.xml): Contains all public & indexable URLs for this website.

## 投稿

- [K-Means聚类算法详解：Python实现与最佳K值确定（肘部法则/轮廓分析）](https://laid-back-scientist.com/k-means)
- [k-means Clustering in Python: Implementation & Determining the Optimal Number of Clusters (Elbow Method & Silhouette Analysis)](https://laid-back-scientist.com/k-means)
- [k-means法のpythonによる実装とクラスター数の決定方法 エルボー法、シルエット分析](https://laid-back-scientist.com/k-means)
- [线性回归与多元回归分析：理论推导、Python从零实现与scikit-learn实战](https://laid-back-scientist.com/multiple-regression)
- [Linear Regression: Multiple Regression Theory and Python Implementation (From Scratch & scikit-learn)](https://laid-back-scientist.com/multiple-regression)
- [線形回帰〜重回帰分析の理論とPythonのフルスクラッチ、scikit-learnによる実装〜](https://laid-back-scientist.com/multiple-regression)
- [线性回归详解：最小二乘法原理、Python从零实现与scikit-learn实战](https://laid-back-scientist.com/least-squares)
- [Linear Regression & Least Squares: Theory and Python Implementation (From Scratch vs. scikit-learn)](https://laid-back-scientist.com/least-squares)
- [線形回帰〜最小二乗法の計算/解説とPythonのフルスクラッチ、scikit-learnよる実装〜](https://laid-back-scientist.com/least-squares)
- [硬间隔支持向量机 (SVM) 原理详解：理论篇](https://laid-back-scientist.com/svm-theory)
- [Hard Margin SVM Explained: Theory and Derivation](https://laid-back-scientist.com/svm-theory)
- [ハードマージンのサポートベクターマシン（SVM）の解説 理論編](https://laid-back-scientist.com/svm-theory)
- [ロジスティック回帰の解説 pythonによる実装と例題](https://laid-back-scientist.com/logistic-imple)
- [向量求导公式推导详解](https://laid-back-scientist.com/vector-diff)
- [Vector Differentiation Formulas: Complete Derivation](https://laid-back-scientist.com/vector-diff)
- [「ベクトルで微分」公式導出](https://laid-back-scientist.com/vector-diff)
- [逻辑回归原理详解：从数学推导到梯度下降](https://laid-back-scientist.com/logistic-theory)
- [Logistic Regression Explained: Theory](https://laid-back-scientist.com/logistic-theory)
- [主成分分析（PCA）详解：Python实现与示例](https://laid-back-scientist.com/pca-imple)
- [Principal Component Analysis (PCA), Python Code](https://laid-back-scientist.com/pca-imple)
- [主成分分析（PCA）の解説 pythonによる実装と例題](https://laid-back-scientist.com/pca-imple)
- [理解主成分分析（PCA）- 理论篇](https://laid-back-scientist.com/pca-theory)
- [Understanding Principal Component Analysis (PCA): Theory](https://laid-back-scientist.com/pca-theory)
- [主成分分析（PCA）の理解 理論編](https://laid-back-scientist.com/pca-theory)
- [二项式定理与多项式定理的证明和例题](https://laid-back-scientist.com/binomial-theorem)
- [The Binomial and Multinomial Theorems: Proofs and Examples](https://laid-back-scientist.com/binomial-theorem)
- [二項定理、多項定理の証明と例題](https://laid-back-scientist.com/binomial-theorem)
- [【統計検定対策】区間推定、信頼区間の求め方とpythonによる実装](https://laid-back-scientist.com/interval-estimation)
- [不等式条件下におけるラグランジュの未定乗数法（KKT条件）](https://laid-back-scientist.com/lagrange-multiplier2)
- [【python】ソフトマージンのサポートベクターマシン（SVM）の実装](https://laid-back-scientist.com/soft-svm-imple)
- [不平衡数据集中的二元分类实践指南](https://laid-back-scientist.com/imbalanced-classification)
- [Practical Guide to Binary Classification with Imbalanced Datasets](https://laid-back-scientist.com/imbalanced-classification)
- [ロジスティック回帰の解説 理論編](https://laid-back-scientist.com/logistic-theory)
- [不均衡データセットにおける二値分類の実践ガイド](https://laid-back-scientist.com/imbalanced-classification)
- [ソフトマージンのサポートベクターマシン（SVM）の解説 理論編](https://laid-back-scientist.com/soft-svm-theory)
- [FastAPIとSQLModelでつくる簡単なToDoアプリ](https://laid-back-scientist.com/fastapi)
- [pytest をつかって FastAPI アプリケーションのテストコードを実装する](https://laid-back-scientist.com/fastapi-test)
- [機械学習システムの設計手法: 1. 要件の具体化](https://laid-back-scientist.com/ml-design-rd)
- [【統計検定対策】中心極限定理の証明とpythonを用いた実装](https://laid-back-scientist.com/clt)
- [图解拉格朗日乘数法：如何求解等式约束下的极值问题](https://laid-back-scientist.com/lagrange-multiplier)
- [Lagrange Multipliers Explained: Optimization with Equality Constraints](https://laid-back-scientist.com/lagrange-multiplier)
- [等式条件下におけるラグランジュの未定乗数法の解説](https://laid-back-scientist.com/lagrange-multiplier)
- [硬间隔 SVM (支持向量机) Python 实现详解：从零手写算法与案例分析](https://laid-back-scientist.com/svm-imple)
- [Hard Margin SVM from Scratch in Python: Implementation & Examples](https://laid-back-scientist.com/svm-imple)
- [ハードマージンのサポートベクターマシン（SVM）の解説 pythonによる実装と例題](https://laid-back-scientist.com/svm-imple)
- [【随机变量的变换】证明与例题](https://laid-back-scientist.com/change-of-variables)
- [Transformation of Random Variables: Proofs and Examples](https://laid-back-scientist.com/change-of-variables)
- [【確率変数の変換】証明と例題](https://laid-back-scientist.com/change-of-variables)
- [二元函数和多元函数的泰勒展开理论 简单证明](https://laid-back-scientist.com/taylor-series3)
- [Taylor Expansion of Two-Variable and Multivariable Functions: Theory and Simple Proof](https://laid-back-scientist.com/taylor-series3)
- [2変数関数、多変数関数のテイラー展開の理論 簡単な証明](https://laid-back-scientist.com/taylor-series3)
- [テイラー展開の理論を解説〜なぜ多項式で表現できるのか〜](https://laid-back-scientist.com/taylor-series)
- [【テイラー展開の例題】exp, sin, cos のマクローリン展開とオイラーの公式の証明](https://laid-back-scientist.com/taylor-series2)
- [【泰勒展开例题】exp、sin、cos 的麦克劳林展开与欧拉公式的证明](https://laid-back-scientist.com/taylor-series2)
- [Maclaurin Series Examples: Expanding exp, sin, cos and Deriving Euler's Formula](https://laid-back-scientist.com/taylor-series2)
- [泰勒展开理论讲解——为什么能用多项式表示](https://laid-back-scientist.com/taylor-series)
- [Understanding Taylor Expansion Theory: Why Can Functions Be Expressed as Polynomials?](https://laid-back-scientist.com/taylor-series)
- [The mechanism of Helmholtz resonance and a simple proof of the resonance frequency theory](https://laid-back-scientist.com/helmholtz-resonance)
- [亥姆霍兹共振的机制与共振频率理论的简单证明](https://laid-back-scientist.com/helmholtz-resonance)
- [ヘルムホルツ共鳴の仕組みと共鳴周波数の理論の簡単な証明](https://laid-back-scientist.com/helmholtz-resonance)
- [t分布の性質 カイ二乗分布・正規分布との関係](https://laid-back-scientist.com/t-dist)
- [SQLFluffでSQLをリントするための設定ファイルをつくる](https://laid-back-scientist.com/sqlfluff)
- [ROC曲線、AURの解説とpythonによる書き方](https://laid-back-scientist.com/roc-curve)
- [機械学習の分類評価指標 混同行列と正解率・適合率・再現率・F値](https://laid-back-scientist.com/confusion-matrix)
- [【Google Cloud (GCP)】Cloud BuildでビルドしArtifact Registryへpushする方法](https://laid-back-scientist.com/gcp-cloudbuild)
- [SQLModel で簡単なテーブルを作成する](https://laid-back-scientist.com/sqlmodel)
- [【Python】poetryでsetup.pyを生成する](https://laid-back-scientist.com/poetry_setup)
- [About events, probabilities and random variables.](https://laid-back-scientist.com/probability)
- [【統計検定対策】多変量データの扱い方 散布図と相関係数](https://laid-back-scientist.com/multivariate-statistics)
- [【Multivariate Data】 Scatter Plots and Correlation Coefficients](https://laid-back-scientist.com/multivariate-statistics)
- [Derivation of Spearman's rank correlation coefficient and example calculation using python](https://laid-back-scientist.com/rank-correlation-coefficient)
- [スピアマンの順位相関係数の導出とpythonを使った計算例](https://laid-back-scientist.com/rank-correlation-coefficient)
- [VSCodeでおこなうpythonのデバッグ方法とlaunch.jsonについて解説](https://laid-back-scientist.com/debug-vscode)
- [【深層学習入門】Kerasによる分類ニューラルネットワークの実装](https://laid-back-scientist.com/classification-nn)
- [【深層学習入門】Kerasによる回帰ニューラルネットワークの実装](https://laid-back-scientist.com/regression-nn)
- [【深層学習入門】Kerasによる画像分類CNNの実装](https://laid-back-scientist.com/cnn-impl)
- [【Google Cloud (GCP)】Compute Engine をCLIで作成](https://laid-back-scientist.com/compute-engine)
- [【Google Cloud (GCP)】Compute Engine のカスタムイメージを作成](https://laid-back-scientist.com/custom-image)
- [【python】logを綺麗にコンソールとファイル出力する自作logger](https://laid-back-scientist.com/log)
- [【Google Cloud (GCP)】Compute Engine のスナップショットを作成](https://laid-back-scientist.com/gcp-snapshot)
- [【統計検定対策】最尤推定・点推定と不偏推定量](https://laid-back-scientist.com/mle)
- [多次元正規分布（多変量正規分布）の線形変換と標準化、積率母関数の証明](https://laid-back-scientist.com/multi-norm)
- [カテゴリカル分布と多項分布 期待値・分散・共分散の求め方](https://laid-back-scientist.com/multinomial)
- [MongoDBとMongo-expressをdocker-composeで立ち上げる](https://laid-back-scientist.com/docker-mongo)
- [【python】DockerでSeleniumを使い動的サイトをスクレイピング](https://laid-back-scientist.com/docker-webscraping)
- [【GCP】Cloud RunでPython FastAPI・Streamlitをデプロイ](https://laid-back-scientist.com/cloud-run-python)
- [【Docker】python + streamlitをmulti-stage buildで構築](https://laid-back-scientist.com/multi-stage-build)
- [【Docker】Ubuntuのタイムゾーンと日本語化の設定](https://laid-back-scientist.com/docker-jp)
- [【Terraform入門】IaCでAWSのEC2を構築する + python環境](https://laid-back-scientist.com/terraform-aws)
- [【MinIO】Amazon S3をローカルで使用する方法](https://laid-back-scientist.com/minio)
- [GCPでJupyterを使ってみる　AI Platform Notebooks](https://laid-back-scientist.com/ai-platform-notebooks)
- [【Git】Sourcetree + P4Mergeの使い方](https://laid-back-scientist.com/sourcetree)
- [【Google Cloud (GCP)】音声をテキストに Speech-to-Text APIの使い方](https://laid-back-scientist.com/speech-to-text)
- [【DynamoDB Local】DynamoDBをローカルで使用する方法](https://laid-back-scientist.com/dynamodb-local)
- [How To Handle Univariate Data Histograms and Box Plots](https://laid-back-scientist.com/univariate-statistics)
- [ROC Curve and AUR, Implementation with Python](https://laid-back-scientist.com/roc-curve)
- [カイ二乗分布の性質 再生性と正規分布との関係](https://laid-back-scientist.com/chi2)
- [【AWS】IAMユーザーの作成とAWS CLIのインストール](https://laid-back-scientist.com/awscli)
- [【Python】Fill In Data With Intervals Between Dates And Times In Pandas.](https://laid-back-scientist.com/fill-datetime)
- [【Python】Creating A List Of Consecutive Dates And Times In Pandas.](https://laid-back-scientist.com/datetime-range-list)
- [Classification Evaluation Indicators: Accuracy, Precision, Recall, F-measure](https://laid-back-scientist.com/confusion-matrix)
- [【Python】Implementation Of Logistic Regression](https://laid-back-scientist.com/logistic-imple)
- [How to install and use labelImg](https://laid-back-scientist.com/labelimg)
- [【Google Colab】How to do object detection and learning with YOLO v5](https://laid-back-scientist.com/yolo-v5)
- [How To Use TA-Lib With Google Colab](https://laid-back-scientist.com/talib)
- [【ImageDataGenerator】Data Augmentation Of Training Images In Keras](https://laid-back-scientist.com/image-data-generator)
- [【AI Platform Notebooks】Using Jupyter With GCP](https://laid-back-scientist.com/ai-platform-notebooks)
- [【Python】Creating a wireframe in Plotly.](https://laid-back-scientist.com/plotly-wireframe)
- [【Python】Plotlyでwireframeを作成する](https://laid-back-scientist.com/plotly-wireframe)
- [【Python, pandas】Delete All/Any 0 Columns And Rows](https://laid-back-scientist.com/pandas-remove0)
- [【Python】pandasで全て0の列と行、一つでも０の列と行を削除する](https://laid-back-scientist.com/pandas-remove0)
- [コーシー・シュワルツの不等式の証明と期待値を用いた表式](https://laid-back-scientist.com/cauchy-schwarz)
- [【Python】About the argument key when sorting](https://laid-back-scientist.com/python-sort)
- [【Python】sortする時の引数keyについて](https://laid-back-scientist.com/python-sort)
- [【統計検定対策】フィッシャー情報量、クラメール・ラオの不等式と有効推定量](https://laid-back-scientist.com/crb)
- [The Distance Between A Point And A Hyperplane](https://laid-back-scientist.com/hyperplane-dist)
- [【統計検定対策】連続的な確率分布 期待値・分散・積率母関数の計算](https://laid-back-scientist.com/pdf)
- [【統計検定対策】大数の法則とモンテカルロ法 pythonによる確認](https://laid-back-scientist.com/law-of-large-numbers)
- [【統計検定対策】離散的な確率分布 期待値・分散・積率母関数の計算](https://laid-back-scientist.com/pmf)
- [【統計検定対策】確率変数の変換と積率母関数](https://laid-back-scientist.com/moment)
- [【統計検定対策】事象、確率と確率変数について](https://laid-back-scientist.com/probability)
- [【統計検定対策】1変量データの扱い方 ヒストグラムと箱ひげ図](https://laid-back-scientist.com/univariate-statistics)
- [【pandas】日時の間隔が空いているデータを埋める](https://laid-back-scientist.com/fill-datetime)
- [【Python】連続した日付、時間のリストの作成する pandas](https://laid-back-scientist.com/datetime-range-list)
- [labelImgのインストール方法と使い方](https://laid-back-scientist.com/labelimg)
- [YOLO v5で物体検出と学習をする方法　Google Colabで動作](https://laid-back-scientist.com/yolo-v5)
- [matplotlibで日本語文字化けの対処法　japanize](https://laid-back-scientist.com/japanize)
- [Google ColabでTA-Libを使う方法](https://laid-back-scientist.com/talib)
- [kerasで学習画像のデータ拡張をする　ImageDataGenerator](https://laid-back-scientist.com/image-data-generator)
- [点と超平面の距離の計算公式の導出](https://laid-back-scientist.com/hyperplane-dist)
- [正規分布](https://laid-back-scientist.com/normal-distribution)

## カテゴリー

- [数学](https://laid-back-scientist.com/category/数学)
- [物理学](https://laid-back-scientist.com/category/物理学)
- [確率・統計学](https://laid-back-scientist.com/category/確率・統計学)
- [機械学習](https://laid-back-scientist.com/category/機械学習)
- [プログラミング](https://laid-back-scientist.com/category/プログラミング)
- [備忘録](https://laid-back-scientist.com/category/備忘録)
- [カテゴリーなし](https://laid-back-scientist.com/category/カテゴリーなし)
- [数学](https://laid-back-scientist.com/category/数学)
- [物理学](https://laid-back-scientist.com/category/物理学)
- [確率・統計学](https://laid-back-scientist.com/category/確率・統計学)
- [機械学習](https://laid-back-scientist.com/category/機械学習)
- [プログラミング](https://laid-back-scientist.com/category/プログラミング)
- [備忘録](https://laid-back-scientist.com/category/備忘録)
- [機械学習](https://laid-back-scientist.com/category/機械学習)
- [数学](https://laid-back-scientist.com/category/数学)
- [物理学](https://laid-back-scientist.com/category/物理学)
- [確率・統計学](https://laid-back-scientist.com/category/確率・統計学)
- [プログラミング](https://laid-back-scientist.com/category/プログラミング)

## タグ

- [python](https://laid-back-scientist.com/tag/python)
- [物理学](https://laid-back-scientist.com/en/tag/物理学-en)
- [数学](https://laid-back-scientist.com/en/tag/数学-en)
- [python](https://laid-back-scientist.com/tag/python)
- [AWS](https://laid-back-scientist.com/tag/aws)
- [DynamoDB](https://laid-back-scientist.com/tag/dynamodb)
- [terraform](https://laid-back-scientist.com/tag/terraform)
- [GCP](https://laid-back-scientist.com/tag/gcp)
- [VSCode](https://laid-back-scientist.com/tag/vscode)
- [Git](https://laid-back-scientist.com/tag/git)
- [S3](https://laid-back-scientist.com/tag/s3)
- [docker](https://laid-back-scientist.com/tag/docker)
- [ubuntu](https://laid-back-scientist.com/tag/ubuntu)
- [MongoDB](https://laid-back-scientist.com/tag/mongodb)
- [python](https://laid-back-scientist.com/tag/python)