# CS229

**Repository Path**: s01edad/CS229

## Basic Information

- **Project Name**: CS229
- **Description**:  My Solutions to Assignments and Projects  Stanford CS229 @Fall 2017
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-15
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Learning CS229
The repo records my solutions to all assignments and projects of Stanford CS229 Fall 2017.

One of many my self-studied courses. You can also check out some of them via belowing links:
- [CS224n Natural Language Processing with Deep Learning, Stanford](https://github.com/LFhase/Learning_CS224n)
- [CS224w Machine Learning with Graphs, Stanford](https://github.com/LFhase/Learning_CS224w)

> *It's the open Internet and the great and kind minds that makes a glance at the beautiful knowledge world possible for us navigators.*

***Thousand Thanks** to those kind and cool people!*

## Introduction 💡
The main learning materials are Fall 2018 class notes and CS229 open course videos.

You can open a new issue or send me a email if you find any mistakes.

The course page is here. http://cs229.stanford.edu/syllabus.html

## Accomplishment 🎈 
- Assignment #1 (😄)
  - problem set: [ps1](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment1/ps1.pdf)
  - solution: [link](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment1/assig1.pdf)
  - source code: [link](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment1/assig1.py)
- Assignment #2 (😄)
  - problem set: [ps2](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment2/ps2.pdf)
  - solution: [link](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment2/assignment2.pdf)
  - source code: 
    - Logistic Regression: [Training stability](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment2/Q1/lr_debug.py)
    -  Spam classification: [Naive Bayes](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment2/Q6/nb.py)
- Assignment #3 (😄)
  - problem set: [ps3](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment3/ps3.pdf)
  - solution: [link](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment3/assignment3.pdf)
  - source code: 
        - K-means: [Image Compression](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment3/Q5/k-means.py)
- Assignment #4 (😄)
  - problem set: [ps4](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment4/ps4.pdf)
  - solution: [link](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment4/assignment4.pdf)
  - source code: 
       - [BP Neural Network](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment4/Q1/nn_starter.py)
       - [ICP](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment4/Q4/bellsej.py)
       - [MDP](https://github.com/LFhase/CS229/blob/master/Assignments/Assignment4/Q6/control.py)
- Project
  - I'll try to solve one of problems in kaggle competition and take it as my class projcet. More details can be seen in my repository.

## Tools 🔨
VS Code <br>
Python 3.6.4 <br>
[PyTorch 1.0.0](https://pytorch.org) <br>
[Tex Live 2018](http://www.tug.org/texlive/windows.html)