Further reading
To learn more, you can check out the following resources:
- Garbin, C., Zhu, X., & Marques, O. (2020). Dropout vs. Batch Normalization: An Empirical Study of Their Impact to Deep Learning. arXiv preprint arXiv:1911.12677: https://par.nsf.gov/servlets/purl/10166570.
- Kandel, I., & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. arXiv preprint arXiv:2003.00204.
- Effect_batch_size_generalizability_convolutional_neural_networks_histopathology_dataset.pdf (unl.pt). Kapoor, A., Gulli, A. and Pal, S. (2020): https://research.unl.pt/ws/portalfiles/portal/18415506/Effect_batch_size_generalizability_convolutional_neural_networks_histopathology_dataset.pdf.
- Deep Learning with TensorFlow and Keras, Third Edition, Amita Kapoor, Antonio Gulli, Sujit Pal, Packt Publishing Ltd.
- Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov...