Back Matter · 2 min read
Notes & Sources
References for the research, tools, and examples discussed in Practical AutoML with Python.
This page collects the main sources used in the book. References are grouped by chapter so you can easily connect each source with the topic it supports. We will continue adding sources as new chapters are published.
Chapter 2 — Why AutoML?
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Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 847–855.
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Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., & Hutter, F. (2015). Efficient and robust automated machine learning. Advances in Neural Information Processing Systems, 28.
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Hutter, F., Kotthoff, L., & Vanschoren, J. (Eds.). (2019). Automated Machine Learning: Methods, Systems, Challenges. Springer. Open-access edition.
Chapter 3 — What Can We Predict with AutoML?
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He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
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Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, 97, 6105–6114.
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Meta AI. (2024). Introducing Meta Segment Anything Model 2.
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Ravi, N., Gabeur, V., Hu, Y.-T., et al. (2024). SAM 2: Segment Anything in Images and Videos. arXiv preprint arXiv:2408.00714.
About These Sources
The references favor original research papers, conference proceedings, publisher pages, and official project pages. Product names such as ChatGPT may appear as familiar examples without a separate citation when no specific technical claim depends on them.
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