bootcamp_machine-learning
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42-AI
Bootcamp to learn basics in Machine Learning
* Day: 05 * Exercise: 08 There is an interlude for ex08 that explains how to vectorize the loss calculation, thus it should be for ex07 and not ex08. Also...
* Day: 05 * Exercise: 06 For the descriptions of the functions `loss_elem_` and `loss_`, the return section says: > None if there is a dimension matching problem between X,...
* Day: 00 * Exercise: 00 There is a commentary for the `__mul__` method that says `returns a Vector if we perform Matrix * Vector multiplication`  But in the...
* Day: 08 (ml_03) * Exercise: 06 (Logistic Regression) The examples provided for the loss function pass X and Y as parameters while the loss function should really be applied...
* Day: 00 * Exercise: 00 In the examples there is a multiplication between a matrix containing `float`s and a vector containing `int`s. The output is written as it was...
* Day: 02 * Exercise: 10 This exercise is way too complicated to implement especially on the school computers. First of all there is a huge gap between the previous...
* Day: 03 * Exercise: 04 "Forbidden functions : **numpy** and any function that performs derivatives for you" What is this supposed to mean? We're manipulating numpy arrays, how can...
* Day: 02 * Exercise: 03 Theta should have shape (4, 1) not (3, 1) since x has shape (., 3) ```python theta1 = np.array([3,0.5,-6]).reshape((-1, 1)) ... theta2 = np.array([0,0,0]).reshape((-1,...
* Module: 03 and others * Exercise: scope=all Some evaluators are very strict about the data type of the result. But there is no mention of a expected data type...
* Day: * * Exercise: * I would like to know if it's possible to remove the iterative way for the calculus since the vectorized way is more efficient and...
