published books
Introduction to Binary Analysis with Radare2
Binary data is data in machine language that cannot be read by humans.
The file of that data is a binary file.
If you are curious, try opening .exe or .dll files on your computer with a text editor such as Notepad. If you are curious, try opening them with a text editor such as Notepad. You have no idea what is written in the file, right?
In this book, we analyze such unreadable binary files mainly using a tool called radare2 and rewrite the binary data, We also describe how to build a linux virtual environment.
At first, you may feel that there are many things you don’t understand, and that it is too difficult and boring, but once you start analyzing, you will be able to understand what is going on.
You will be fascinated by the beauty of the “black screen” and “Christmas-colored strings,” and you will find yourself falling in love with the binary data.
How to learn machine learning on your own with chainer
This book describes the study content and the machine learning models implemented by a new SE graduate who studied machine learning on his own and became a machine learning engineer after about one year of study. This book explains what he studied and the machine learning models he implemented when he studied on his own.
So what should you study in this book? The mathematics used in machine learning, the explanation of the learning algorithms of machine learning, and more. The book is packed into one volume! Furthermore, the book introduces the implementation of machine learning without programming, so even non-engineers can build machine learning models!
With this book in your hands, maybe you too can become a machine learning engineer next year!
Learning Python with Manga
In 2020, I believe that the Corona has caused people to spend more time at home.
In this book, I would like to introduce a new concept for the time spent at home.
I want to provide you with a fun and educational content that you can read manga and learn programming (Python).
I wrote this book in order to provide you with content that is both fun to read and educational. I wrote this book in order to provide you with content that is both fun to read and educational.
I hope that everyone who picks up this book will find their life of self-restraint as meaningful as possible.
When you learn programming for the first time, it is very difficult to understand the coding rules, algorithms, and mechanisms of programming. I think that many people feel that there is a barrier in understanding the coding rules, algorithms, and mechanisms of programming when studying programming for the first time.
Therefore, in this book, I will explain such basic knowledge of programming, but I will focus on creating something and making it work, so you can enjoy programming while having fun.
I believe that this is a book where you can enjoy programming at the same time.
How to learn machine learning on your own with pytorch
This book explains what a new graduate SE from a technical college studied on his own to become a machine learning engineer after about a year of study, and the machine learning models he implemented when he studied on his own. What do I need to study? The book is packed with explanations of the mathematics used in machine learning and the learning algorithms of machine learning. Furthermore, it also introduces the implementation of machine learning without programming, so even non-engineers can build machine learning models.
【Agenda】
Chapter 1: Major mathematical knowledge used in machine learning
Chapter 2: Python Environment and Language Learning
Chapter 3: Learning Deep Learning from scratch
Chapter 4: Building a learning model using NNC
Chapter 5: Chainer
Chapter 6: Building a Machine Learning Environment Using Cloud Services
Chapter 7: Pytorch
Academic Papers
A Multivariate Causal Discovery
【Abstract】
Understanding causal relations of systems is a fundamental problem in science. The study of causal discovery aims to infer the underlying causal structure from uncontrolled observational samples. One major approach is to assume that causal structures follow structural equation models (SEMs), such as the additive noise model (ANM) and the post-nonlinear (PNL) model, and to identify these causal structures by estimating the SEMs. Although the PNL model is the most general SEM for causal discovery, its estimation method has not been well-developed except for the bivariate case. In this paper, we propose a new causal discovery method based on the multivariate PNL model. We extend the bivariate method to estimate multi-cause PNL models and combine it with the iterative sink search scheme used for the ANM. We apply the proposed method to synthetic and real-world causal discovery problems and show its effectiveness.
Skills related to IT technology
- Provided front-end website development using WordPress, Wix, and other editing software.
- Developed technical and non-technical marketing presentations, public relations campaigns, articles, and newsletters.
- I built a system to automate employee management and other HR systems using GAS and SNS APIs.
- Provided front-end website development using VueJS, TypeScript, Docker, GCP and other editing software.
- Reviewed technical and professional publications and journals to stay current on recent literature and make more strategic research decisions.
- Performed accurate quantitative analysis of targeted data research, collection and report preparation.
- Conferred with scientists, engineers or others to plan or review projects or to provide technical assistance.
- Collaborated with leadership team to identify relevant questions and determine best methods of collection.
- Interpreted data analysis results to draw inferences and conclusions.
- I had introduced container development using docker and a CI/CD system.
- Maintained office PCs, networks and mobile devices.
- Reviewed project specifications and designed technology solutions that met or exceeded performance expectations.
- Worked with software development and testing team members to design and develop robust solutions to meet client requirements for functionality, scalability, and performance.
- Updated old code bases to modern development standards, improving functionality.
- Participated in architecture, design and implementation of back-end features using C++, C#, Lua and Python.
- Developed intricate algorithms based on deep-dive statistical analysis and predictive data modeling.
- Identified, measured and recommended improvement strategies for KPIs across business areas.
- Set up SQL database on cloud servers to store client data for query analysis.
- Analyzed large datasets to identify trends and patterns in customer behaviors.
【Development Languages】
Python, R, C++, C#(.Net), PHP, postgres SQL, MySQL, JSP, Java, JavaScript, terraform(IaC & MLOps), VueJS, TypeScript, HTML, CSS
【Development Tools】
GCP, AWS, Docker, Gitlab CI/CD, Linux, CentOS, Visual Studio, Eclipse, Jenkins, Wireshark, Apatch, VMWare, Hyper-V
【Management Tool】
Agile, Redmine, Slack, Backlog, WBS (Work Breakdown Structure)