Machine Learning Implementation

Description

This is a library of machine learning implementations for quick reference.

Implementations

- Tabular Classification -

Binary Classification

• MLP
• Bagging and Voting

Multi-class Classification

• MLP
• Bagging and Voting

Multi-label Classification

• MLP

- Tabular Regression -

• MLP

- Image Classification -

Multi-class Classification

• CNN

- Image Generation -

MNIST

• WGAN

- Text Classification -

Binary Classification

• Pretrained model + RNN

- Graph Classification -

Node Classification

• GCN

- Reinforcement Learning -

FrozenLake

• Value Iteration
• Policy Iteration
• Monte Carlo

CartPole

• DQN (Double DQN, Dueling DQN, PER, n-step reward)

LunarLander

• DQN (Double DQN, Dueling DQN, PER, n-step reward)

Installation

To install the necessary packages, run the following command in your terminal:

pip3 install -r requirements.txt

We recommend installing the CUDA-enabled version of PyTorch, which can be found here

Usage

To use a specific implementation, navigate to the main folder and run the corresponding [filename]_main.py file like this:

python3 [filename]_main.py

Example

To run the GCN example on the Cora dataset, run the following command:

python3 graph_convolutional_network_cora_main.py