This is an excellent book. I have hundreds of papers and books on Neural Nets from the time of Rosenblatt's Perceptron on through autoencoders, recurrent NNs, convolutional NNs, RBM's, DNN's, greedy pretraining, Kolmogrov's universal approximation theorem, optimization methods for weight training, and more. I found this book to provide a conceptual overview of the DNNs and the architectures (feed forward, deep belief, unsupervised pre-trained, convolutional, recurrent, long and short term memory, and recursive, networks). The book provides the conceptual connective tissue that are the muscles that the practitioner must bond to the architectural bones to move forward in Deep Learning. The book is a remarkable debrief by two lead developers of the DL4J framework; Josh Patterson and Adam Gibson.