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license: apache-2.0 |
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## My Notes 📓 |
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This repository contains my lecture notes from graduate school on following topics 👇🏼 |
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- Data Science: 8 cheatsheets |
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- Machine Learning (follows [Tom Mitchell's book](http://www.cs.cmu.edu/~tom/mlbook.html)): 25 pages of notes |
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- Statistics: 9 cheatsheets |
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- Deep Learning: 12 cheatsheets, will upload more |
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- Image Processing (follows [digital image processing book](https://www.amazon.fr/Digital-Image-Processing-Rafael-Gonzalez/dp/013168728X)): 21 cheatsheets |
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- Data Structures and Algorithms (follows [this book by Goodrich](https://www.wiley.com/en-us/Data+Structures+and+Algorithms+in+Python-p-9781118549582)): 26 cheatsheets |
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✨ *Some notes* ✨ |
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- Most of these notes aren't intended to teach a topic from scratch but are rather notes that I took and compiled during my midterm & finals, might help you remember things, study for exams, and prepare for job interviews. |
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- There might be very small Turkish notes in few of the pages, you can ignore them. |
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- I will upload more notes as I find or create them. Will soon compile my Hugging Face cheatsheets so stay tuned! |
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- It's appreciated if you could improve the quality of PDF handwritten scans or convert them to JPEG, you can open a PR to this repository. |
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*Updates* 🎉 |
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- I uploaded hierarchical clustering and improved version of K-means. |
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- I compiled every lecture in separate PDFs, and also compiled those into single PDF, found under `Compiled PDF`s. |
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- I uploaded Hugging Face cheatsheets. |
