This repository contains Jupyter notebooks and code examples for the book "Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann.
This repository contains a collection of Jupyter notebooks and code examples that demonstrate how to use PyTorch for deep learning tasks. The notebooks cover a range of topics, including:
- Basic PyTorch operations and syntax
- Building and training neural networks
- Convolutional neural networks (CNNs) for image classification
- Recurrent neural networks (RNNs) for sequence modeling
- Generative adversarial networks (GANs) for image generation
- Transfer learning and fine-tuning pre-trained models
- Deploying PyTorch models to production
The notebooks are organized by chapter and section of the book, making it easy to follow along with the examples and exercises in the text.
My environment is described in the file requirements.txt. The whole setup is on Ubuntu 20.04.2 on WSL2.
Of course these notebook are meant to be complementary to the book, so you should have a copy of the book to follow along.
To use the notebooks in this repository, clone the repository to your local machine:
git clone https://github.com/Nordiniv/deep-learning-with-pytorch-notebooks.git
Then, navigate to the repository directory and start Jupyter Notebook:
cd deep-learning-with-pytorch-notebooks
jupyter notebook
This will open Jupyter Notebook in your web browser. You can then navigate to the notebook you want to run and click on it to open it.
The project covered in part 2 will be done separately in its own repository.
The notebooks in this repository are based on the examples and exercises from the book "Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann. We would like to thank the authors for their excellent work and for making their code available to the community.
