I would like to join.
I appreciated the history of deep learning presented in the introduction. It goes a bit further back than what you can find on Wikipedia. I thought the discussion of representations was illuminating, more so than the YouTube videos I have seen that attempt to explain how neural networks work. The distinction between computation and probabilistic models, in my estimation, could have been clearer, since only the former was illustrated. I'm also left wondering why deep learning can only handle nested hierarchies of concepts, when it seems likely that other entity relationships are possible. Lastly, I look forward to learning more about algorithms in the field, but in examining other chapters I am not seeing any examples of how we will translate the various mathematical concepts into code.
I've wanted to read this book as well. Thanks for coming up with this idea. I'll gladly join the conversation.