This is a table of free resources to help fill in some knowledge gaps that might exist.
| Resource | Description | Link |
|---|---|---|
| CS 285 | This is the Deep Reinforcement Learning, Decision Making, and Control course at UC Berkeley | The main course page can be found here CS285. |
| CS 231n | This is the Convolutional Neural Networks for Visual Recognition course at Stanford | The main course page can be found here CS231n. |
| The Missing Semester of Your CS Education | This is a course offered at MIT inteded to fill gaps in the CS education that covers a lot of "expected" knowledge | The main course page can be found here missing-semester. |
| Introduction to Computational Thinking | This is a course offered at MIT designed to introduce computational thinking using Julia. | The main course page can be found here comp-thinking. |
| How To Read a Paper | You will be reading a lot of papers in your PhD; here is a paper telling you how to do it | https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf |
| How to Write a Paper | Papers are the primary output of researchers. This website gives some guidance on how to write one | https://cs.stanford.edu/people/widom/paper-writing.html |
| How to review a paper | You should start reviewing papers for conferences and journals during your time as a student. This paper gives some instructions | https://www.cs.utexas.edu/users/mckinley/notes/reviewing-smith.pdf |
| SLAM and Estimation | Cyrill Stachniss has a youtube channel with many introductory mini-lectures | https://www.youtube.com/channel/UCi1TC2fLRvgBQNe-T4dp8Eg |
| Neural Network Training | Andrej Karpathy has an excellent blog post on training Neural Networks that includes common pitfalls | http://karpathy.github.io/2019/04/25/recipe/ |