Same core algorithm as Forward Search, but instead of calculating a utility based on the action-value over all possible next states, you make \(m\) different samples of next state, action, and reward, and average them

A bunch of matricies could be sparse; for fluid dynamics, for instance, has a \(10^{6} \times 10^{6}\) matrix, but may only have \(7 \times 10^{6}\) non-zero entries; but the inverse could be fully dense!

In these cases, we almost never want to form a in inverse if needed.

If we really need to invert this, performing a LU-Factorization is going to be a very good idea.

• take audio

• calculate Mel Scale representation

• apply a series of Filter Banks which attenuates the input to highlight groups of frequencies

  

• we then run a discrete-cosine transform to obtain MFCCs, because much of the output results will still correlate with each other

Group Meetings

A spinal tap is a medical procedure whereby cerebralspinal fluid is collected by puncturing the lumbar; used to diagnose problems where biomakers from the brain are needed.