Section 1.4
Further Reading and Resources

The following is a list of recent textbooks that augments that material covered in these lecture notes:

• Goodfellow et al., Deep Learning, 2016 (http://www.deeplearningbook.org/)
• Zhang et al., Dive into Deep Learning, 2023 (http://https://d2l.ai/)
• Prince, Understanding Deep Learning, 2023 (https://udlbook.github.io/udlbook/)
• Scardapane, Alice’s Adventures in a Differentiable Wonderland, 2024 (https://www.sscardapane.it/alice-book)

You should also familiarize yourself with the PyTorch deep learning library (https://pytorch.org/), which has excellent documentation and tutorials to work through.

Finally, there are some very high quality lectures and courses online from various institutions, including:

• Stanford (Fei-Fei, Karpathy, et al.), https://cs231n.stanford.edu/
• Michigan (Johnson), https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/
• York University (Derpanis), https://www.eecs.yorku.ca/~kosta/Courses/EECS6322/
• University of Amsterdam (Lippe), https://uvadlc-notebooks.readthedocs.io/en/latest/