I'm looking forward to this book and I'm assuming that the goal here is to delve into reinforcementlearning, unlike almost all other ML/DL books. I personally care about the game of Go only because it will help me learn RL.
I noticed that the 88 pages published so far (which I quickly scanned) are just building up to neural networks. My personal preference is that very basic knowledge of deep learning should be assumed (since manning has several books which introduce the subject). I don't mind a review, but I hope the book is mostly about reinforcement learning, rather than deep learning with RL being relegated to one or two chapters.
I've had trouble understanding RL so far because the few other books out there overexplain relatively simple to understand concepts such as MDPs but jump right over the mishmash of equations for qlearning, utility calculaiton, etc. These equations often have recursion as well as iteration and although they probably look simple to someone who already understands them, they are quite difficult to understand initially. RL becomes even more confusing because authors very quickly go over various iterations: value iteration, policy iteration, sarsa, table blah blah that it leaves a newbies head spinning!
I realize this book will show how to use deep learning + RL but I wouldn't mind getting some historical context for how RL was used before deep learning.
Looking forward to learning more about RL, I personally find it to be MUCH more interesting than other branches of ML, and just as confusing!
