Fixes #9598

The MLP class implements a simple feedforward neural network for classification. It has an input layer, a hidden layer with a tanh activation, and an output layer.

The init() method initializes the weight matrices and bias vectors randomly.

The forward() method performs the feedforward pass, calculating the outputs from the input data using the weights/biases.

The fit() method trains the network using backpropagation and gradient descent. It:

Makes predictions
Calculates the loss
Computes gradients of the loss with respect to the weights/biases
Updates the weights/biases to reduce the loss
This allows the MLP to learn non-linear functions approximators and fit to training data for tasks like classification. The network is trained iteratively using backprop and gradient descent to minimize the loss.

In summary, the MLP class implements a basic neural network that can be trained on data to perform machine learning tasks by learning to make data-driven predictions and decisions.

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".