I know there are quite a few good deep learning books out there, but most explain neural networks and deep learning via application on images. If there are examples/code, they are often done on the MNIST data set.
I was wondering if there's a book out there that goes in depth into neural networks and is equally well written but explains it on non-image data. I am particularly interested in time series application, although some talk on cross-sectional application would be helpful as well. I am particularly interested in learning about:
- What types of layers/functions/structure are better suited for time-series data
- Various models for time series and pros/cons/applications of each (Convoluted NN, LTSM, etc...)
- Typical structures of your neural network (depth, sparse connections, etc...) that seem to work well on time series data
- Special considerations or settings you should have in your neural network when working with time series data
- Maybe some talk/examples on how time series prediction using traditional models like ARIMA, can be reproduced or done better using neural networks. Or side by side comparison of pros/cons of using one vs the other.