New script demonstrates the implementation of the Sigmoid, tanh function, Matrix determinant and Matrix trace calculation.

[Mathasuriya-infy]

The sigmoid function is a logistic function, which describes growth as being initially exponential, but then slowing down and barely growing at all when a limit is reached. It's commonly used as an activation function in neural networks.

Added implementation of the Hyperbolic Tangent (Tanh) activation function to the neural network activation functions collection.
The tanh function is a hyperbolic function that maps any real-valued input to a value between -1 and 1. It's commonly used as an activation function in neural networks and is a scaled version of the sigmoid function.

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• 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".