What will be covered:
- Solution to the numpy + MNIST + MLP assignment
- Git primer
- Theano primer
- Porting numpy + MNIST + MLP to Theano
Slides can be found here. You can bring your laptop to follow along if you’d like to.
If you haven’t already, it would be a very good idea to create a Github account for the lecture.
A student pointed out that the batch version of the outer product used in the solution could be implemented using a dot product:
V_grad = (H[:, :, None] * (Y - T)[:, None, :]).mean(axis=0)
V_grad = numpy.dot(H.T, Y - T) / H.shape
for instance. Not only is it simpler to write and understand, but it also greatly speeds up computation. The assignment solution has been updated accordingly.
Thanks for the intervention!