[Table of Contents] Copying "A Half-Step Beyond Linear Algebra" in Python

Book Author: Dr. Jun Okubo

Types of Copying
I will implement the book's mathematical formulas and charts in Python.

Introduction to the Book

This series is a Python implementation (copying) of the book "A Half-Step Beyond Linear Algebra: 30 Chapters Before Tackling Data Science and Machine Learning" (authored by Jun Okubo, published by Kodansha Scientific, hereafter referred to as the "Text").

The text was released in March 2025, and as the subtitle "Before Tackling Data Science and Machine Learning" suggests, it is a mathematics book that serves as a bridge between linear algebra and data science.

Table of Contents

Please see each article via the underlined links!

Part 0 (Articles outside the book)

📘Introduction: "Started Python Copying"

Part 2: Interpreting Arranged Numbers [Basis Expansion of Functions]

📗Chapter 1: "Hermite Polynomials, Legendre Polynomials, and Fourier Series Expansion"

Part 3: Applying Arranged Numbers [Data Science and Machine Learning]

📗Chapter 2: "Linear Regression, Ridge Regression, and Lasso Regression"

📗Chapter 3: "Singular Value Decomposition, Eigenvalue Decomposition, and Principal Component Analysis"

Part 4: Arranged Numbers and the Changing World [Matrices and Time-Evolution Systems]

📗Chapter 4: "Time-Evolution Equations, Differential Equations, and Partial Differential Equations"

Part 5: Further Developments in Arranged Numbers [Linearity in Nonlinear Systems]

📗 Chapter 5: "Van der Pol Equation, Snapshot Pairs, Kolmogorov Backward Equation"

The following chapters are related to the next two episodes.

📗 Chapter 6: "Koopman Operator Theory, Dynamic Mode Decomposition"

📗 Chapter 7: "Koopman Operator Theory, Dynamic Mode Decomposition, Future Prediction"

The following chapters put the ChatGPT-crafted "Koopman Theory & DMD Complete Master" into practice!

📙 Chapter 8: "Extended Dynamic Mode Decomposition, Kernel Dynamic Mode Decomposition"

📙 Chapter 9: "Kolmogorov Backward Equation, DMD Time-Series Clustering"

📙 Chapter 10: "DMD Video Analysis, DMD Review"

Table of Contents for the Fun Copying Series

Blog Introduction

I am writing eight series of articles on note.
Please come and take a look!

1. Relaxed Statistics

This is a blog that roughly explores probability and statistics using the Statistical Test Grade 2 problem collection as a guide.
Feel free to read it like casual conversation. Please come and take a look.
It corresponds to the Statistical Test Grade 2 Official Problem Collection CBT version.
Python and EXCEL sample code are also available for distribution.

2. Connecting with Statistics and Data Analysis

The series "Connecting with Statistics and Data Analysis" is a blog series written in a column style that uncovers the "connections" to statistics and data analysis.
With the help of generative AI, I wrote it freely, imagining the gateway to statistics and data analysis.
For example...
- Connections between daily life and statistics
- Connections from Statistical Test Grade 2 to what lies beyond
I hope you enjoy reading it at your leisure 🍀

3. Experiment! Fun Bayesian Modeling 1 & 2 with PyMC Ver. 5

We will draw and analyze the Bayesian models used in psychological research from the books 'Fun Bayesian Modeling' and 'Fun Bayesian Modeling 2' using PyMC Ver. 5.
Like this book, many Bayesian models are written in the R language and Stan.
I strive to explore the possibilities of PyMC and make Bayesian modeling easily accessible.
These are familiar and easy-to-visualize themes, so please try running them in PyMC and let's enjoy it together!

4. Experiment! Bayesian Modeling from Iwanami Data Science Vol. 1 with PyMC Ver. 5

We will draw and analyze the Bayesian models by four Bayesians from the book 'Experiment! Iwanami Data Science Vol. 1' using PyMC Ver. 5.
This book is an excellent resource for learning the basics of Bayesian programming.
I feel like I've become friends with Bayesian methods by happily running PyMC models.
Everyone, please try running them in PyMC and let's play and learn together!

5. Fun Copying: Bayesian, Python, etc.

I will blog about the results of my 'book copying activities' for Bayesian, Python, and other topics.
I am mainly working on translations into Python.
I hope this serves as sample code for fellow learners who are also copying code. 🍀

6. Introduction to Time Series Analysis for Psychology with R and Stan, using Python and PyMC Ver. 5

We will practice the time series analysis from the book 'Introduction to Time Series Analysis for Psychology with R and Stan' using Python and PyMC Ver. 5.
This book is packed with themes on time series analysis!
I realized the depth of time series analysis.
I will enjoy learning time series analysis with my favorite language, Python.

7. Writing about Data Science-like things

I write columns on statistics, data analysis, AI, machine learning, and Python on an irregular basis.
There are many articles related to statistics and data science books.
Series on 'Statistics', 'Python', 'Mathematics and Python', and 'R' have been created.

8. Practical Notes on Python Machine Learning Programming

I wrote articles about my various thoughts when studying the book 'Python Machine Learning Programming: PyTorch & scikit-learn Edition'.
This book is a textbook for scikit-learn and PyTorch.
Please feel free to give it a try.

Thank you very much for reading until the end.