AI • Data Science• Intelligent Systems

Deepsim Press

Publishing high-quality resources on AI, Data Science, Signal Processing, and practical Python workflows. Bridging research, applications, and education.

What is Deepsim Press

Deepsim Press is a technical publishing imprint and communication platform operated by Deepsim Intelligence Technology Inc., Canada. It is dedicated to publishing high-quality works at the intersection of mathematics, data science, artificial intelligence, and real-world systems.

Deepsim Press publishes carefully curated books, technical resources, and research-oriented materials that prioritize clarity, depth, reproducibility, and long-term relevance over trends or superficial coverage.

Featured Books

Financial, Security, and Digital Systems

Book Series

Wavelet Transform in Practice

From Theory to Production-Ready Python Application

The series of Wavelet Transform in Practice: From Theory to Production-Ready Python Application presents a comprehensive exploration of wavelet theory, computational methods, and real-world applications. Across five volumes, the series guides readers from the mathematical foundations of multiscale analysis to advanced wavelet techniques and domain-specific applications in scientific, environmental, financial, and digital systems. Each volume combines clear conceptual explanations with practical Python implementations to support reproducible and interpretable data analysis.

Practical Data Science with Python

Modern Workflows for Analytics, Machine Learning, and Data Engineering

Practical Data Science with Python is a hands-on series that guides readers through the complete modern data science workflow, from data analysis and visualization to machine learning, forecasting, and production-ready engineering. Using practical examples and modern Python tools such as Pandas, Polars, PySpark, scikit-learn, DuckDB, and Streamlit, the series emphasizes scalable workflows, reusable systems, and real-world problem solving. Designed for analysts, developers, and aspiring data scientists, the series focuses not only on tools and algorithms, but also on building reliable, maintainable, and efficient data science solutions in practice.

Author

Bridging advanced theory with real-world, production-ready systems

Dr. Shouke Wei earned his Ph.D. in Environmental and Resource Management at Brandenburg University of Technology Cottbus–Senftenberg (Germany), conducted postdoctoral research at the Swiss Federal Institute of Aquatic Science and Technology (Eawag, Switzerland ), and held research positions at the University of British Columbia (Canada). He has held distinguished and adjunct professorships at multiple institutions (China).

He is the founder of Deepsim Intelligence Technology Inc., Deepsim Academy, and Deepsim Press. He currently serves as Chief Scientist and Editor-in-Chief across these institutions.

In addition, he is Director of the Qilu Artificial Intelligence and Digital Manufacturing Innovation Laboratory, and serves as Director and Postdoctoral Co-Supervisor at the Shandong Postdoctoral Innovation Practice Base at Deepsim Intelligent Technology Co., Ltd., China.

He has participated in or led 19 international research projects and has been invited as a keynote speaker at 15 scientific conferences and workshops. His achievements include 30 software copyrights, 6 patents, and 48 publications, comprising 40 academic papers and 8 books. He has also authored over 500 tutorial blog articles, contributing extensively to knowledge dissemination in artificial intelligence and data science.

External Research Identifiers

👉 “ORCID:” https://orcid.org/0000-0002-4665-5366

👉 “ResearchGate:” https://www.researchgate.net/profile/Shouke-Wei

👉 “Google Scholar:” https://scholar.google.ca/citations?user=WMANSd8AAAAJ

👉 “GateHub Repository:” https://github.com/shoukewei

👉 “Zenodo DOI Repository :” https://zenodo.org/deepsim-do

Book Stores

👉 “Browse All eBooks” → internal book store (Deepsim Press )

👉 “Browse All Printed Books” → external store (Amazon )