3.6 Further reading
There are a lot of great resources to learn more about the essential building blocks of deep learning. Here are just a few popular resources that are a great start:
Nielsen, M.A., 2015. Neural networks and deep learning (Vol. 25). San Francisco, CA, USA: Determination press., http://neuralnetworksanddeeplearning.com/.
Chollet, F., 2021. Deep learning with Python. Simon and Schuster.
Raschka, S., 2015. Python Machine Learning. Packt Publishing Ltd.
Ng, Andrew, 2022, Deep Learning Specialization. Coursera.
Johnson, Justin, 2019. EECS 498-007 / 598-005, Deep Learning for Computer Vision. University of Michigan.
To learn more about the problems of deep learning models, you can read some of the following resources:
Overconfidence and calibration:
Guo, C., Pleiss, G., Sun, Y. and Weinberger, K.Q., 2017, July. On calibration of modern neural networks. In International conference on machine learning (pp. 1321-1330). PMLR.
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley...