## Understanding transposed convolutions in PyTorch

This post explains transposed convolution and relevant module arguments in PyTorch.

Number-Smithy

A Blog of Programming, Algorithms and Software Tools

Number-Smithy

A Blog of Programming, Algorithms and Software Tools

This post explains transposed convolution and relevant module arguments in PyTorch.

In this post we put together all the building blocks covered in previous posts to create a convolution neural network, using numpy, and test it on the MNIST hand-written digits classification task.

This post covers the derivations of back-propagation in a convolution layer, with numpy implementations.

This post will share some knowledge of 2D and 3D convolutions in a
convolution neural network (CNN), and 3 implementations all done using pure `numpy` and `scipy`.

The convolution functions in `scipy` do not work well with missing data. We create a 2D convolution function that allows a controllable tolerance to missing values. It is first implemented in Fortran, then using `scipy` in an FFT approach.