# What is notes

The notes for Math, Machine Learning, Deep Learning and Research papers.

## Objective

![image](https://1712266326-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LMrEcS7cR9bGTHSnCnB%2F-LPLpSRRjkebpJP1wX5T%2F-LPLpSy5Wmgiu_tRO38g%2Fdata_to_wisdom.jpg?generation=1540128901637817\&alt=media)

 Illustration by [David Somerville](http://www.smrvl.com/blog/) based on the original by [Hugh McLeod](https://twitter.com/gapingvoid/statuses/423952995240648704)

* 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.