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Corrected reference to exercise4.m in Part 3.4 to exercise3.m #5

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Corrected reference to exercise4.m in Part 3.4 to exercise3.m #5

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4 changes: 2 additions & 2 deletions doc/instructions.md
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Expand Up @@ -529,14 +529,14 @@ What you have just witnessed is actually a fairly general principle: centring th

Now we will explore several parameters in the algorithms:

> **Task:** Restore the preprocessing as given in `experiment4.m`. Try the following:
> **Task:** Restore the preprocessing as given in `experiment3.m`. Try the following:
>
> * Try increasing the learning rate `eta`. Can you achieve a better value of the energy in the 500 iterations?
> * Disable momentum by setting `momentum = 0`. Now try to beat the result obtained above by choosing `eta`. Can you succeed?

Finally, consider the regularisation effect of shrinking:

> **Task:** Restore the learning rate and momentum as given in `experiment4.m`. Then increase the shrinkage factor tenfold and a hundred-fold.
> **Task:** Restore the learning rate and momentum as given in `experiment3.m`. Then increase the shrinkage factor tenfold and a hundred-fold.
>
> - What is the effect on the convergence speed?
> - What is the effect on the final value of the total objective function and of the average loss part of it?
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