Resources

This page collects books, tools, references, and technical resources that I have found useful across mathematics, machine learning, signal processing, scientific computing, and biomedical data analysis.

It is also structured so it can grow into a long-term reference page for future posts and recommendations.

Mathematics and Foundations

• Linear Algebra by Gilbert Strang
• Convex Optimization by Boyd and Vandenberghe
• Pattern Recognition and Machine Learning by Christopher Bishop
• Deep Learning by Goodfellow, Bengio, and Courville

Signal Processing and Fourier Analysis

• Discrete-Time Signal Processing by Oppenheim and Schafer
• The Fourier Transform and Its Applications by Bracewell
• NIST Digital Library of Mathematical Functions

Scientific Computing and Data Work

• Python
• NumPy
• SciPy
• pandas
• matplotlib
• Jupyter

Research Workflow Tools

• Git and GitHub
• Jekyll / GitHub Pages for scientific writing and personal websites
• LaTeX for mathematical writing
• Zotero for reference management

Biomedical and Statistical Work

• R and Python statistical ecosystems
• causal modelling and mediation analysis references
• interpretable machine learning tooling
• reproducible reporting workflows

Notes

This page currently lists core references and tools only. Over time I may expand it with:

• recommended books
• software tools
• datasets
• tutorials
• article collections
• selected external links relevant to my research and blog posts

For collaboration or technical support related to these areas, see the Services page.