https://github.com/dongjunlee/notes
The notes for Math, Machine Learning, Deep Learning and Research papers.
https://github.com/dongjunlee/notes
computer-vision deep-learning generative-model machine-learning natural-language-processing notes optimization reinforcement-learning summary
Last synced: 6 months ago
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The notes for Math, Machine Learning, Deep Learning and Research papers.
- Host: GitHub
- URL: https://github.com/dongjunlee/notes
- Owner: DongjunLee
- Created: 2017-12-17T05:33:01.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2019-09-29T13:16:23.000Z (about 6 years ago)
- Last Synced: 2025-04-15T00:13:44.550Z (6 months ago)
- Topics: computer-vision, deep-learning, generative-model, machine-learning, natural-language-processing, notes, optimization, reinforcement-learning, summary
- Language: Python
- Homepage: https://humanbrain.gitbook.io/notes/
- Size: 24.7 MB
- Stars: 52
- Watchers: 9
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# notes [](https://github.com/hb-research) [](https://github.com/hb-research)
The notes for Math, Machine Learning, Deep Learning and Research papers.
## Objective
Illustration by David Somerville based on the original by Hugh McLeod
- Let's make **wisdom** from knowledge.
- Define concepts to be intuitively understandable.
* Simply summary (You can check the details on Wiki)
* With `story` or example
* Draw an `illustration`
* If possible, append a `code`
- ~~Documentation by [Gitbook](https://humanbrain.gitbook.io/notes/)~~
- Documentation by [Notion](https://www.notion.so/Machine-Learninig-5e1a0088828045e995b07f34a05a614a)
## Usage
- Sync papers (* recommend path like Google Drive's sync folder)
```
python scripts/sync_papers.py {SYNC_PATH}
```
- Make `SUMMARY.md`
```
python scripts/make_summary.py
```
---
## Knowledge Source
### Math
- Course & Video
* [Statistics 110: Probability - Projects at Harvard](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo)
* [Mathematics for Machine Learning: Linear Algebra by David Dye](https://www.coursera.org/learn/linear-algebra-machine-learning)
### Machine Learning
- Course & Video
* [Stanford University - Machine Learning](https://www.coursera.org/learn/machine-learning) by Andrew Ng.
* [Stanford University - Probabilistic Graphical Models](https://www.coursera.org/course/pgm) by Daphne Koller
* [OXFORD University - Machine Learning](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
### Deep Learning
- Book
* [Deep Learning](http://www.deeplearningbook.org/) by Ian Goodfellow Yoshua Bengio and Aaron Courville, 2016
- Course & Video
* [Stanford University - CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/index.html) by Fei-Fei Li, Andrej Karpathy, Justin Johnson
* [Udacity - Deep Learning](https://www.udacity.com/course/deep-learning--ud730) by Vincent Vanhoucke, Arpan Chakraborty
* [Toronto University - Neural Networks for Machine Learning](https://www.coursera.org/course/neuralnets) by Geoffrey Hinton
* [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/index.html) by Richard Socher
* [Deep Learning School (bayareadlschool)](http://www.bayareadlschool.org/) September 24-25, 2016 Stanford, CA
* [Oxford Deep NLP 2017](https://github.com/oxford-cs-deepnlp-2017/lectures) by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.