# stanford-cs229

**Repository Path**: M7He/stanford-cs229

## Basic Information

- **Project Name**: stanford-cs229
- **Description**: 🤖 Exercise answers to the problem sets from the  2017 machine learning course cs229 by Andrew Ng at Stanford
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-23
- **Last Updated**: 2021-04-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Introduction

This repo records my answers to all questions from the excercises of CS229
(Autumn 2017). http://cs229.stanford.edu/syllabus.html

I tried to record all details in Jupyter notebooks. If you see any
mistake, please let me know by
[opening a new issue](https://github.com/zyxue/stanford-cs229/issues/new?template=your-question-or-bug-report.md).

As for reinforcement learning, I've also implemented value iteration, policy
iteration, SARSA, and Q-learning  before in javascript for the gridworld at
https://github.com/zyxue/rljs with a web demo at https://rljs.herokuapp.com/.

I find some of the homeworks in an earlier version
(https://see.stanford.edu/Course/CS229) of this course interesting, so I chose
to do some and placed the answers in the `previous_cs229` fold.


# Usage

For non-interactive visualization of the notebooks, you could either read them
on github directly, or use http://nbviewer.jupyter.org/ for somewhat better
quality.

If you'd also like to modify the notebooks without setting up a local server,
you may give https://mybinder.org/ a try.


# Development

Create virtual environment:

```
conda env create --prefix venv -f env-conda.yml
```

Start the server

```
jupyter notebook --no-browser --ip 0.0.0.0
```

Export virtual environment:

```
conda env export --prefix venv > env-conda.yml
```


# About LaTeX

For interactive LaTeX editing, you could use
https://www.codecogs.com/latex/eqneditor.php.