12 · Wrap-Up and Take-Homes
The close of Tensors for Machine Learning. This section has two jobs: connect the ideas from all thirteen sections, and hand you the extensions.
One idea connects sections 07, 09 and 10: when a problem has no exact answer and no true inverse, you do not give up. You find the best stable approximation instead.
You watch it happen three times — the pseudoinverse on a real linear system in section 07, Tucker on an oversized tensor in section 10, and Richardson-Lucy on a blurred photograph in take-home F.
The take-homes are appendices A to F: PCA, attention, CP vs Tucker, Cholesky, audio denoising, and convolution/deconvolution.
Study here, then run the real thing: notebook 12 on Colab · notebook 13, the convolution deep dive · the handbook
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