showml 1.5.4

A Python package of Machine Learning Algorithms implemented from scratch

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Show the Machine Learning Code

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A Python package of Machine Learning Algorithms implemented from scratch.

The aim of this package is to present the working behind fundamental Machine Learning algorithms in a transparent and modular way.

NOTE: The implementations of these algorithms are not thoroughly optimized for high computational efficiency.

📝 Table of Contents

• Getting Started
• Contents
• Contributing

🏁 Getting Started

Install the package:

$ pip install showml

To clone the repository and view the source files:

$ git clone https://github.com/hasnainroopawalla/ShowML.git
$ cd ShowML
$ pip install -r requirements.txt

Check out: showml/examples/

📦 Contents

Models

• Linear

  • Linear Regression (showml.linear_model.regression.LinearRegression)
  • Logistic Regression (showml.linear_model.regression.LogisticRegression)

• Non-Linear

  • Sequential (showml.deep_learning.model.Sequential)

Deep Learning

• Layers

  • Dense (showml.deep_learning.layers.Dense)

• Activations

  • Sigmoid (showml.deep_learning.activations.Sigmoid)
  • ReLu (showml.deep_learning.activations.Relu)
  • Softmax (showml.deep_learning.activations.Softmax)

Optimizers

• Stochastic/Batch/Mini-Batch Gradient Descent (showml.optimizers.SGD)
• Adaptive Gradient (showml.optimizers.AdaGrad)
• Root Mean Squared Propagation (showml.optimizers.RMSProp)

Loss Functions

• Mean Squared Error (showml.losses.MeanSquaredError)
• Binary Cross Entropy (showml.losses.BinaryCrossEntropy)
• Categorical Cross Entropy (showml.losses.CrossEntropy)

Simulations

• Conway's Game of Life (showml.simulations.conways_game_of_life.GameOfLife)

✏️ Contributing

1. Fork the repository.
2. Install the necessary dependencies:

$ pip install pre-commit mypy pytest

3. Commit and push your changes to your own branch.
4. Before submitting a Pull Request, run these housekeeping checks locally:-

• Run pre-commit:

$ pre-commit run -a

• Run mypy:

$ mypy .

• Run pytest:

$ pytest