PyTorch Experiments Repository

This Git repository contains a single Jupyter notebook that encompasses a series of experiments using PyTorch for various tasks. The notebook covers the following experiments:

Experiment Overview

1. Image Classification with PyTorch:

• Utilizes PyTorch for image classification. The experiment involves training a convolutional neural network (CNN) on a dataset to demonstrate image classification capabilities.

2. Variational Autoencoder Training:

• Implements and trains a Variational Autoencoder (VAE) using PyTorch. The notebook showcases the process of generating new data points using the learned latent space.

3. Neural Transfer: Kanagawa to Falcon 9 Image:

• Demonstrates Neural Transfer techniques using PyTorch. The experiment focuses on transferring style features from the famous Kanagawa wave image to a Falcon 9 launch image, showcasing artistic style transfer capabilities.

4. ANN Regressor on Sonar Dataset:

• Trains an Artificial Neural Network (ANN) regressor on the Sonar dataset. The notebook illustrates how PyTorch can be employed for regression tasks.

Getting Started

To run these experiments, follow these steps:

1. Clone the repository:

   git clone https://github.com/vtocitu365/torch_template.git

2. Open and run the Jupyter notebook (*.ipynb) in your preferred environment.

Dependencies

The notebook relies on the following dependencies:

• PyTorch
• Matplotlib
• NumPy

Make sure to have these dependencies installed before running the notebook.

Contributing

If you have suggestions or improvements, feel free to open an issue or submit a pull request. Your contributions are highly valued.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Happy experimenting with PyTorch!