Modern reference on general Deep Learning theory
For the latest practices of deep learning architectures, I follow the Kaggle latest Competition's notebooks.
Say, for example, In the recent finished Cassava Leaf Deasese Computer Vision Competition, People are sharing the experimental notebooks on different State of the art Architectures Like Vision Transformer(Various versions pretrained  - vit_large_patch16_384, vit_base_patch16_384 etc. models), Facebook's Data-efficient Image Transformers , EfficientNet (Noisy Student version, Imagenet Version) and etc.
 https://www.deeplearningbook.org/ this book undoubtedly one of the great book. But for the latest practices if you follow the Kaggle notebooks you get a good explanation with codes.
I suggest you,  Explore this Notebooks Section of the Casava Leaf Disease Classification Contest to get more Models and Various recent new released Loss Functions and so on.