# What is notes

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

## Objective

![image](/files/-LPLpSy5Wmgiu_tRO38g)

 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.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://humanbrain.gitbook.io/notes/master.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.