AI class errata
These are my notes on errata I've discovered in the AI class coursework.
Videos
| Video | Time | Is | Should be |
|---|---|---|---|
| 4G5mH4FW-WY | 0:51 | curvatic | quadratic |
| 0RmqLOxexh4 | 0:26 | corresponding | closed-form |
| 0RmqLOxexh4 | 0:41 | interation | iteration |
| 0RmqLOxexh4 | 0:54 | up with it | update it |
| rAcwpZJqAZA | 0:46 | local minimum | local minima |
| dKKigX6nhyU | 0:05 | quadratic arrow | quadratic error |
| R1o9wbhnv94 | 0:43 | plausible | closed-form |
| yOSGC67bOIk | 0:50 | sets | data sets |
| yOSGC67bOIk | 1:53 | grade descent | gradient descent |
| yOSGC67bOIk | 1:57 | grade descent | gradient descent |
| yOSGC67bOIk | 3:10 | and the error is zero, then no update occurs | then the error is zero, and no update occurs |
| xRf9wAeU1kI | 1:43 | robust-ness | robustness |
| xRf9wAeU1kI | 2:00 | integer | iterative |
| xRf9wAeU1kI | 3:32 | so that just | plotted just |
| xRf9wAeU1kI | 3:49 | Map | Mapped |
| xRf9wAeU1kI | 4:36 | to write | to derive |
| xRf9wAeU1kI | 4:44 | These messages | These methods |
| ZLEilYyt28c | 0:35 | condition probabilities | conditional probabilities |
| PoRpuj4bijU | 0:00 | is an easy answer | is easily answered |
| tOSoqfK9UNE | 1:05 | If your graph input | If you graph your input |
| tOSoqfK9UNE | 1:35 | they are to be one | there ought to be one |
| tOSoqfK9UNE | 1:36 | your're | you're |
| kFwsW2VtWWA | 0:04 | are seen not to be completely random determinants | seem not to be completely randomly determinate |
| EZEOXNFgu8M | 0:08 | interpretively assume | typically assume |
| EZEOXNFgu8M | 0:38 | structure and data | structure in data |
| EZEOXNFgu8M | 1:21 | drawing signal | joint signal |
| W2dkDmHFMWg | 2:04 | They were derived | They will be derived |
| zaKjh2N8jN4 | 0:18 | found interatively | found iteratively |
| zaKjh2N8jN4 | 0:34 | Euclidian | Euclidean |
| zaKjh2N8jN4 | 1:28 | has attained the center | is attained at the center |
| myqnyxkdQpc | 0:17 | corresponding step | correspondence step |
| myqnyxkdQpc | 1:10 | local minimum | local minima |
| 3zlXl82LUVI | 0:02 | one interation | one iteration |
| _DhelJs0BFc | 0:44 | horizontal access | horizontal axis |
| _DhelJs0BFc | 2:04 | then it is | than it is |
| _DhelJs0BFc | 2:08 | periphery summary over here | periphery somewhere over here |
| rMcw3uu4efY | 1:55 | complete the derivative for spectrum mu | compute the derivative with respect to mu |
| rMcw3uu4efY | 2:03 | we can still get this | we instead get this |
| rMcw3uu4efY | 2:11 | next to zero | it's still zero |
| rMcw3uu4efY | 2:56 | stresses internal | is just its internal |
| pRGEQy7BgiY | 0:04 | multivariant | multivariate |
| mlz-1yfyeoU | 0:06 | the fit from data | how to fit them from data |
| 1CWDWmF0i2s | 0:47 | Their movement is smooth away | They move in a smoother way |