Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 2022 (MIT Press) - This standard textbook provides comprehensive coverage of data structures and algorithms, including complexity analysis (O(n), O(1)), hash tables, lists, and trees, all of which are essential background for understanding ML performance.
Designing Machine Learning Systems, Chip Huyen, 2022 (O'Reilly Media) - This book offers a practical perspective on building machine learning systems, emphasizing how efficient data handling and algorithmic choices impact system performance, scalability, and resource requirements in real-world ML applications.
Feature Hashing for Large-scale Multitask Learning, Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, Josh Attenberg, 2009Proceedings of the 26th International Conference on Machine Learning (ICML) (International Machine Learning Society (IMLS))DOI: 10.48550/arXiv.0902.2206 - This paper introduces feature hashing, a method for transforming high-dimensional data into a lower-dimensional representation using hash functions, which is directly referenced in the section for efficient feature engineering.
Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - Authored by the creator of Pandas, this book explains how libraries like Pandas leverage efficient underlying data structures (built on NumPy arrays) for fast data loading, preparation, and manipulation, as highlighted in the section.