bootcamp_machine-learning
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42-AI
Bootcamp to learn basics in Machine Learning
* Day: 03 * Exercise: 03 'The purpose of epsilon (eps) is to avoid log(0) errors, it is a very small residual value we add to y. ' should be...
* Day: 06 * Exercise: 03 Given examples of `loss_elem_` are 10 times greater than it should. \ The subject says: ```python # Example 0.1: lr1.loss_elem_(y, y_hat) # Output: array([[710.45867381],...
* Day: 05 * Exercise: 01 (TinyStatistician) The correction page gives this test: ```python import TinyStatistician as ts data = [42, 7, 69, 18, 352, 3, 650, 754, 438, 2659]...
* Day: 09 * Exercise: 03 (closed) The `reg_log_loss_` function should have an epsilon as it's parameter ## Fixed on: - [ ] Github - [ ] Gitlab
* Day: 08 * Exercise: 04 The loss_ function should be taking y and y_hat, but the example uses X, Y **Screenshots**  ## Fixed on: - [ ] Github...
* Day: 07/02 * Exercise: Multipe `predict_(X, [...])` instead of `predict_(x, [...])` ## Fixed on: - [ ] Github - [ ] Gitlab
* Day: 02/07 * Exercise: 02(closed)/03(normal)  thetas are incorrect They should be `theta1 = np.array([0.0,3,0.5,-6]).reshape((-1, 1))` and `theta2 = np.array([0.0,0,0,0]).reshape((-1, 1))` ## Fixed on: - [ ] Github -...
* Day: 01 * Exercise: 01 The example uses default thetas of `1` instead of `1.0` stopping the training for happening, the cast not working  ## Fixed on: -...
* Day: 01 * Exercise: 00 Subject function name and example don't match  function is named `simple_gradient` but is called as `gradient` ## Fixed on: - [ ] Github...
The pipelines builds the files but won't push them. This is a minor fix, juste to make it more convenient.