A repo containing useful things for the MSc AI course at Imperial. Please feel free to add to this repo any links/code/books you have found useful while on the course.

Cheatsheets --> git, conda, pandas, regex and more!
**Books --> Artificial Intelligence - A Modern Approach, Bayesian Optimization, Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges, Graph Representation Learning, Information Theory, Inference, and Learning Algorithms, Pattern Recognition And Machine Learning, Deep Learning, Dive into Deep Learning, Mathematics for Machine Learning, Reinforcement Learning.
HPC GPU Submission Scripts --> Gpucluster, High performance cluster
Pratical Imperial Guides --> Intro to the HPC
Other Useful Scripts --> wandb sweep example yaml file

• PapersWithCode: Benchmark comparisons of state-of-the-art algorithms, with code provided
• Distill.pub: Fun interactive AI papers
• Tools to keep on top of new research:
  1. Semantic Scholar: Create customized research feeds (using AI), see paper summaries and much more!
  2. Arxiv Sanity: Arxiv filtering based on favorited papers
  3. Aminer: Similar to semantic scholar; excellent feature is relevant literature tree for papers (see example)
  4. Connected Papers: view relevant papers in a connected graph.
• List of top computer science conferences Guide to research
• Up and coming AI conference deadlines AI deadlines
• Magical tool that allows you to screen shot any equation and turn it into latex code!!!! Mathpix
• Repo to check GPU availability at Imperial thanks to @afspies repo
• Tool to draw convolutional neural nets link
• Useful tool to visualise and experiment with your regex expressions link
• Repo detailing some advanced python concepts beyond the Imperial Python Programming course. Thanks to @sirvan3tr repo. work in progress
• Nice step by step guide by @afspies for remote DoC environment setup.
• Nice collection of need to know papers in NLP, vision, speech, core ML and RL link

• Deep RL: The group present publications in reinforcement learning with a particular interest for deep RL. Previous sessions covered topics such as robot control, hierarchical RL, exploration, planning and multi-agent systems.
• Machine Learning: All machine learning related content with a mathematical focus.
• Language and Multimodal AI Lab (LAMA): NLP related papers discussed.

Please feel free to contribute to this repo. If you would like to do so e.g. adding a useful link to the README it would be good to stick to good git pratice:

1. Fork this repo
2. Create a branch
3. Commit and push your changes
4. Create pull request
5. Someone will review your changes and merge them.

note this is great practice for contributing to open source code!

Create an issue and someone will respond!

If you find this repo useful, please consider giving it a ⭐