January 2019
Intermediate to advanced
294 pages
6h 43m
English
So far, the model has been randomly initialized and with this we have been able to get an output. In order to assess if the actual output is close to the desired output, loss function is introduced. It enables the generalization of the model, and figures out how well the model is able to reach the desired output.
We can have a look at the new table, which has got actual output as well as desired output:
| Input (X) | Actual Output (Ya) | Desired Output (Y) |
| 2 | 6 | 4 |
| 3 | 9 | 6 |
| 4 | 12 | 8 |
| 5 | 15 | 10 |
| 6 | 18 | 12 |
If we have to put the loss function down, it has to be as follows:
Loss Function = Desired Output-Actual Output
However, putting loss function this way would invite both kinds of values: negative and positive. In the case of ...
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