Practical / Modern & Applied ML + Deep Learning Resources

 

Resource Why it’s useful / What you get
Dive into Deep Learning (Aston Zhang, Zachary C. Lipton, Mu Li, Alexander Smola) — freely available online Blends theory + hands-on code (via Jupyter notebooks) — ideal for applying ML and deep learning in practice, especially with Python. (arXiv)
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar — free PDF/book Focuses on interpretability and explainability of ML models — increasingly important for real-world ML/deployment. (originalstatic.aminer.cn)
Data Science and Machine Learning: Mathematical and Statistical Methods (by Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre & Radislav Vaisman) — 2024 edition Covers mathematical and statistical methods underlying ML, useful for bridging theory and applied data-science workflows. (people.smp.uq.edu.au)

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