Gsoc https://summerofcode.withgoogle.com/organizations/4507228564881408/ 2018 just ended one week ago and we had many successefully completed student projects https://github.com/BoostGSoC18. I was responsible for adding tensor support to Boost.uBLAS for primarily supporting multilinear algebra operations in the field of numerics. The wiki description along with the implementation can be found here https://github.com/BoostGSoC18/tensor. Similar to Boost.multi_array https://www.boost.org/doc/libs/1_68_0/libs/multi_array/doc/index.html the runtime-reshapable tensor data structure is parametrized in terms of number of dimensions (rank/order), dimension extents, data type, layout (first- and last-order) and storage type. The first two are runtime-variable. I am also about to add subtensor (view/handle of a tensor) along with multidimensional iterators for convenient algorithm implementation. It is yet not as flexible as GSL's multi_span https://github.com/Microsoft/GSL/blob/master/include/gsl/multi_span: does not yet support static rank and dimensions. However, basic generic tensor operations (contraction/transposition/reshaping/...), including a nice syntax for Einstein's summation convention with placeholders, using C++17 features are provided. The operations are evaluated using expression templates (not smart yet). Similar to the tensor https://eigen.tuxfamily.org/dox/unsupported/group__CXX11__Tensor__Module.htm... framework of Eigen, that is used by tensor flow https://github.com/tensorflow/tensorflow, the tensor data structure in Boost.uBlas could be taken for implementing deep neural networks or higher-order statistics I think. I am not sure if the C++ community would appreciate if Boost has some form of basic operations for building *deep neural networks* (DNNs). I would like to ask 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? Cem