[outrun86]

Bishop’s Pattern Recognition and Machine Learning is one example that has tremendous depth and much clearer notation. Deep Learning by Goodfellow et al. is another example, albeit with less depth than Bishop.

I’m glad you’re enjoying the book. The approach is ideal for a very small subset of the ML population, no doubt that was their intention. I’m just weighing in that it’s entirely possible to cover this material with rigour yet much simpler notation. Even as someone who could parse this I’d go with other options.

[jeffhwang]

Thanks for highlighting Bishop to me! I've self-taught through various resources esp. Goodfellow et al 2016. It's taken me a number of years to rebuild my math knowledge so that I feel comfortable with Goodfellow's treatment and look forward to learning from the Bishop book. Fwiw, I've found the math notation in the Goodfellow textbook to be among the best I've ever seen in terms of consistency and clarity. Some other books I enjoy, for example, do not seem to make any typographic indication of whether an object is a vector, scalar, or other. :(

[p1esk]

FYI, Bishop just released an updated DL book: https://www.bishopbook.com/

[HybridCurve]

I appreciated the notation in Goodfellow book as well, it was easy enough for me to follow without having a strong mathematics background. I'll agree however with others that this text is instead focused for a different audience and purpose.