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Ground-up implementations of ML algorithms

Overview

This project contains a curated collection of machine learning and deep learning algorithm implementations that are written from scratch (i.e., minimal dependencies), intended for learning, experimentation and research. The implementations are designed to work with popular frameworks like PyTorch and TensorFlow, along with GPU‑compatible training scripts and datasets.

Project Structure

Ground-up-implementations-ML-algorithms-/
├── algorithms/                         # Core algorithm implementations
│   ├── MLP_cifar_pytorch.py            # MLP using PyTorch
│   ├── MLP_mnist_tf.py                 # MLP using TensorFlow
│   ├── CNN_cifar_pytorch.py            # CNN using PyTorch
│   ├── CNN_mnist_tf.py                 # CNN using TensorFlow
├── dataset/                            # Dataset loader
├── notebooks/                          # Jupyter notebooks demonstration
│   ├── MLP_cifar_pytorch.ipynb         # MLP using PyTorch
│   ├── MLP_mnist_tf.ipynb              # MLP using TensorFlow
│   ├── CNN_cifar_pytorch.ipynb         # CNN using PyTorch
│   ├── CNN_mnist_tf.ipynb              # CNN using TensorFlow
│   ├── RNN_cifar_pytorch.ipynb         # RNN using PyTorch
│   ├── RNN_mnist_tf.ipynb              # RNN using TensorFlow
└── LLM_scratch.ipynb                   # LLM using PyTorch
├── LICENSE                             # MIT License
├── README.md

Featured Algorithms

# Algorithm Framework Dataset Code Train Time Accuracy
1 Multilayer Perceptron PyTorch CIFAR10 Github Colab 14 min 0.46
2 Multilayer Perceptron Tensorflow MNIST Github Colab 22 s 0.96
3 Convolutional Neural Network PyTorch CIFAR10 Github Colab 22 min 0.63
4 Convolutional Neural Network PyTorch MNIST Colab 2 min 0.96
5 Convolutional Neural Network Tensorflow MNIST Github Colab 12 s 0.98
6 Recurrent Neural Network From Scratch Book by
H.G. Wells "The Time Machine"		 Colab 20 min Perplexity = 1.2
7 Recurrent Neural Network PyTorch Book by
H.G. Wells "The Time Machine"		 Colab 3 min Perplexity = 1.1
8 Large Language Model (Using Generative Pre-Trained Transformers) PyTorch Edith Wharton's
"The Verdict"		 Colab 28 s Perplexity = 1.9

Code Usage

  1. Clone the repository:
git clone https://github.com/rastri-dey/Ground-up-implementations-ML-algorithms-.git
cd Ground-up-implementations-ML-algorithms-
  1. Install dependencies:
pip install -r requirements.txt
  1. Run a model script or notebook:
python -m algorithms.<algorithm_script>

Note: Some models include Google Colab links to launch with minimal setup.

Contribution

We welcome contributions that:

  • Improve algorithm correctness or efficiency
  • Add new models or datasets
  • Provide notebooks with visual explanations
  • Improve documentation

Just open a Pull Request or Issue and we’ll help you get started!

License

This project is licensed under the MIT License.