2 Resources and References
Joel Davis, Ph. D.
- Plot Hierarchical Clustering Dendrogram. scikit-learn.
- Ioffe, S. & Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (2015).
- Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs] (2019).
- Pandas – Python Data Analysis Library.
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014).
- Keras: The Python deep learning API.
- Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning. (MIT Press, 2016).
- Matplotlib — Visualization with Python.
- MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges.
- NumPy.
- Python.org. Python.org.
- R: The R Project for Statistical Computing.
- RStudio Open source & professional software for data science teams.
- Scikit-learn: Machine learning in Python — scikit-learn 1.1.dev0 documentation.
- TensorFlow.
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