There are multiple books on deep learning currently available. I'm primarily interested in the theory and algorithms and less interested in the "practical guide" books really just tell me how to use a specific deep learning library (like PyTorch or TensorFlow) and black-box the mathematical derivations. I've seen various books recommended to me:
- Deep Learning by Aaron C. Courville, Ian Goodfellow, and Yoshua Bengio
- Online book at http://neuralnetworksanddeeplearning.com by Michael Nielsen
- Neural Networks and Learning Machines, 3rd edition, by Simon Haykin
- Neural Networks: A Systematic Introduction by Raúl Rojas
I've read through the Courville-Goodfellow-Bengio book, and I though it covered a wide variety of topics. I'm wondering if it's still worth reading the other books. Could someone who has read these books do a compare-and-contrast on them? Particularly, I'm wondering if its still worth reading the other books given what I have already read, or if reading them would just go over content that I've already seen in the first book and be redundant.