Hi, I am also a beginner in machine learning and I did some research on the associated hardware.
First up the CPU, I recommend getting a ThreadRipper CPU, a 1900X will be enough as TensorFlow (as you mentioned) mainly use CUDA cores for computation. You could use CPU for this but the difference will be night and day compare to GPU. The features we want from a TR system is the PCIE lanes, and more importantly the quad channel memory. Often I read other researcher mentioned they have their learning speed bottle-necked at the RAM speed, if your RAM is not fast enough to keep up with your GPU, more PCIE bandwidth will not help either.
For the extra PCIE lanes, it will be useful if you planning to have a multi acceleration card configuration in the future. But from what I read configuring multi card system could potentially be very tedious on the software side. So keep in mind on the future upgradability and scalability.
For the RAM size, the rule of thumb is at least double the size of you VRAM, so the 32 GB will be more than enough in your case of Titan Xp.
For the motherboard, I recommend buying one with 10gbps nic port. However, this depends on your future plan. Many lab with super large data set, they like to setup an external JBOD (Just a Bunch of Disk) for storing their data, with a certain degree of striping and redundancy. In this case the bottleneck will happened on your nic port. Or if you would like to store all your data within your computer case, I would recommend a SAS controller for expanding storage.
All and all, everything I mentioned is highly depending on the data set you are working on. If you are not planning on future upgrade, a simple system with enough PCIE lanes will do. If you experience bottle-necking in the future, check your code/algorithm/data set, as there are always more ways to optimize on software level before upgrading your hardware. Bottle-necking may not always happened on your hardware.