For a book published in late 2014, it's very strange that this book doesn't seem to mention any of the state of the art components in neural networks, including:

   - Convolutional neural networks 
   - LSTMs
   - GRUs 
   - Autoencoders 
   - Attentional mechanisms 
   - Variations on SGD (Adagrad, Adadelta, RMSProp, ADAM)

Baby Steps -

You will be surprised how little knowledge people have about neural nets ( speaking as someone who did Applied Maths in school ).

Outside reading deep learning news, I've been away from neural nets for something like ten years. The table of contents looks like it presents some of the older approaches. Does it similarly cover what you need to learn the newest stuff? Or do we read stuff like this plus other resources for that?