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1. M. Bernstein, “Labeling and Crowdsourcing,” Knowledge-Centric AI, accessed June 13, 2022, https://datacentricai.org.
2. N. Diamant, E. Reinertsen, S. Tune, et al., “Patient Contrastive Learning: A Performant, Expressive, and Practical Approach to Electrocardiogram Modeling,” PLOS Computational Biology 18, no. 2 (Feb. 14, 2022): 1-16.
3. S. Brown, “Why It’s Time for ‘Data-Centric Artificial Intelligence,’ ” MIT Sloan College of Administration, June 7, 2022, https://mitsloan.mit.edu.
4. M.D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in “Pc Imaginative and prescient — ECCV 2014,” eds. D. Fleet, T. Pajdla, B. Schiele, et al. (Zurich: Springer, 2014), 824.
5. A. Kolesnikov, L. Beyer, X. Zhai, et al., “Big Transfer (BiT): General Visual Representation Learning,” arXiv, Could 5, 2020, https://arxiv.org.
6. T. Chen, S. Kornblith, M. Norouzi, et al., “A Simple Framework for Contrastive Learning of Visual Representations,” arXiv, Feb. 13, 2020, http://arxiv.org.
7. Ibid.
8. F. Chollet, “Deep Studying With Python,” 2nd ed. (Shelter Island, New York: Manning Publications, 2021).
9. A. Ng, “MLOps: From Mannequin-Centric to Knowledge-Centric AI,” PDF file (Palo Alto, California: DeepLearning.AI, June 2021), www.deeplearning.ai.
10. Chollet, “Deep Studying With Python.”
11. DeepLearning.AI, “A Chat With Andrew: MLOps: From Model-Centric to Data-Centric AI,” YouTube video, 1:00:10, March 4, 2021, www.youtube.com.
12. D. Wang, A. Khosla, R. Gargeya, et al., “Deep Learning for Identifying Metastatic Breast Cancer,” arXiv, June 18, 2016, https://arxiv.org.
13. Chollet, “Deep Studying With Python.”
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