What's a good explanation of Tensor Flow for someone living under a rock? I dismissed it as some machine learning library, but I read it is in fact a general computing framework. If I can use it for things like numerical integration or some numpy-type tasks, that would be interesting.
Tensorflow does general computation using data flow graphs; you assemble your graph from operations and variables (tensors, as they're called these days), and Tensorflow handles distribution of this computation over hardware which you make available.
A quick google gives these [1] impressive results for Tensorflow, at least for linear algebra operations.
Despite the advantages, I think you'll find many more readily available functions in Numpy for what you want, while Tensorflow remains quite 'low level', exposing building block operations rather than higher-level methods (the exception is machine learning/neural network stuff). That said, I don't imagine it would be too difficult to implement a fast quadrature method for integration, or whatever else your heart might desire. This [2] is a simple example solving a PDE.
