I totally agree. DNNs are just one part. Lets put in more numerical algorithms using tensors, matrices and vectors. However, IMHO I think that the community needs better documentation, interfaces, (possibly implementation) of basic tensor, matrix and vector operations. I am now also changing the interface of the tensor data structure for being able to be an alias for matrix and vector. Am Sa., 1. Sep. 2018 um 11:54 Uhr schrieb David Bellot < david.bellot@gmail.com>:
1. if it make sense for boost to support basic operations for DNNs? 2. what are the obligatory, necessary basic operations for creating DNN building blocks? 3. if there are any additional data structure parameters that needs to be added for (efficiently) supporting DNNs?
DNN are certainly interesting models in machine learning, but they represent a very small part of what we can do and what really works in real life. Tensors can be applied to many more situations. On top of it, tensors can be applied to many other field of science.
What would be interesting is to start thinking more generically and not focus too much on just DNN, especially if we want to have machine learning in ublas (which was another successful GSOC this year by the way).
I'm glad to see we have tensors, I'm happy to see we have basics stats and ML. Let's go to the next step and have some more numerical techniques related to linear algebra in ublas.
Open discussion now .....
David