I remember taking my first Deep Learning course in University 5 years ago; We had to implement the neural network, gradient computation, Batch Normalization, Dr

I remember taking my first Deep Learning course in University 5 years ago; We had to implement the neural network, gradient computation, Batch Normalization, Drop out and all other details from scratch without external libraries in either Java/C++/Matlab. Apparently this was the old way DL used to be taught, and even in 2016 the professor insisted everyone had to do it this way. I didn't study ML in general so what I gained from the course was a deep understanding of the fundamentals & math behind it, but since I didn't get to familiarize myself with any of the existing libraries (Tensorflow/Keras back then) I had a hard time convincing anyone in industry of my skills in the field :/ Also: Why does the book only cover Deep Q-Network on Reinforcement Learning? Sure it is the most notable deep learning step in the field but, there are some relevant versions such as Actor-Critic & Maximum Entropy RL that can be very relevant too. If one includes YOLO, ResNet and newer architectures for Computer Vision application, I don't know why same things are not on RL.