Contains all the content to be used for the AI Journal Club

The upcoming AI journal club will start off every meeting with a small intro to machine learning, deep learning and/or reinforcement learning tutorial. This will include a basic introduction with code.

Following the instructional time, we'll begin discussions on an actual modern paper. This section is primarily discussion based and will highlight modern papers in the field.

There will also be time and space dedicated towards project creation.

Welcome to the official Repo for the AI journal club! The AI journal club will be hosting weekly meetings (both online and in person) where we'll teach basic deep learning neural networks as well as discuss recent papers in deep learning.

This repo will be updated as time goes on with the different lessons for that day related to what we will be teaching. The early lessons will go over PyTorch and deep learning and then later on, we'll be going over and building some more complex modern deep learning neural networks involving CNNs, RNNs, LSTMs, GNNs, etc.

Throughout this, we'll also be pairing people up to work on projects together to give more exposure to deep learning. Projects will then be presented later towards the end of the AI journal club. More details will be provided closer to the date.

To use this repo, fork the repo and follow along with the contents of the tutorials. The papers will also be linked in the lessons for that week as well.

We'll be primarily using Python, PyTorch, VS Code, and Jupyter Notebooks. Prior to coming to the journal club, ensure you have those up and running! Alternatively, you could also use Google Colab. If you plan on downloading PyTorch, we won't be doing anything too intensive to feel free to download the CPU version as the GPU version may come with a slew of difficulties that may be challanging to troubleshoot.

We'll need a couple of additional Python libraries including:

• PyTorch
• Numpy
• Pandas
• Matplotlib

There may be other libraries we download down the line that may be a bit more difficult to install or may simply be optional:

• Pillow
• OpenCV
• TensorFlow
• Keras

To learn a bit more about setting up all these libraries, check out the setup.ipynb notebook in lessons. The notebook is written in a Jupyter Notebook and thus if you're not familiar with how Jupyter works, you can also check out JupyterNotebooks.md to learn more about how to view them and run them.

To take part in the journal club, the best approach would be to fork this repository and add to your own version of this repo as we go. There will be plenty of space for us to fill in information as we go along and you'll also be able to host your projects in your repos as well.

If you see an error or bug that may need to be fixed, start off by creating an issue for it in GitHub! If you need to fix something, start off by creating a different branch for the issue and create a pull request. Then, request to merge to main and put down a detailed description of the changes in the merge request as well as assign @priyanshumahey as a reviewer to ensure that the changes are allowed.