References

1. A. W. Trask, Grokking Deep Learning (Manning, Shelter Island, NY, 2019).
2. F. Chollet, Deep Learning with Python. Manning Publications, 2021.
3. Y. Cui, M. Jia, T.-Y. Lin, Y. Song, and S. Belongie, “Class-Balanced Loss Based on Effective Number of Samples,” p. 10.
4. K. Cao, C. Wei, A. Gaidon, N. Arechiga, and T. Ma, Learning Imbalanced Datasets with Label- Distribution-Aware Margin Loss [Online]. Available at https://proceedings.neurips.cc/paper/2019/file/621461af90cadfdaf0e8d4cc25129f91-Paper.pdf.
5. R. Jantanasukon and A. Thammano, Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems. In 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, Cha-am, Thailand: IEEE, Mar. 2021, pp. 229–232, doi: 10.1109/ECTIDAMTNCON51128.2021.9425715.
6. H.-J. Ye, H.-Y. Chen, D.-C. Zhan, and W.-L. Chao...