some kind of an ml library in cuda

rough timeline:

• feedforward neural network
• manual gradient calculation for backward pass
• automatic gradient calculation
• add convolution expression
• convolutional neural network

...some intermediate steps, then eventually:

• transformer

somewhere along the line:

• get close to cuBLAS performance on SGEMM
• decide how to benchmark/ compare speed

task for initial steps: MNIST dataset