Machine Learning and Deep Learning Algorithms with NumPy

This repository contains implementations of various machine learning and deep learning algorithms using NumPy. Unlike typical implementations that rely on library APIs, the focus here is on understanding the basic principles and implementations of algorithms. At the moment, training aspects are not covered, and the code may not be necessarily rigorous or efficient; it is intended solely for learning and reference purposes.

Python Version: 3.9

NumPy Version: 1.22

Algorithm Directory

• Machine Learning Algorithms (located in the MachineLearning folder):

  • Kmeans
  • K-Nearest Neighbors (KNN)
  • To be updated...

• Deep Learning Algorithms (located in the DeepLearning folder):

  • Maxpool
  • CNN
  • Dropout
  • BatchNorm
  • LSTM
  • Bilstm
  • To be updated...

Usage

To use the implementations in this repository, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/StarJulian/Machine_DLearning_With_NP.git
   

2. Navigate to the directory containing the desired algorithm implementation.

3. Open the Python file in your preferred development environment (e.g., Jupyter Notebook, Spyder, VS Code).

4. Follow the instructions and comments in the code to understand the implementation and usage of the algorithm.

Acknowledgments

Special thanks to contributors and the open-source community for their valuable contributions and insights.

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