coursera-ml-julia
所属分类：人工智能/神经网络/深度学习
开发工具：Julia
文件大小：0KB
下载次数：0
上传日期：2016-02-16 15:01:37
上 传 者：
sh-1993
说明： Coursera斯坦福机器学习课程（Julia）
(Coursera Stanford Machine Learning course in Julia)
文件列表:
LICENSE.md (1164, 2016-02-13)
REQUIRE (68, 2016-02-13)
appveyor.yml (1161, 2016-02-13)
src/ (0, 2016-02-13)
src/coursera-ml.jl (0, 2016-02-13)
src/data.jl (288, 2016-02-13)
src/ex1/ (0, 2016-02-13)
src/ex1/LinearRegression.jl (353, 2016-02-13)
src/ex1/computeCost.jl (633, 2016-02-13)
src/ex1/computeCostMulti.jl (671, 2016-02-13)
src/ex1/ex1-contour.js.svg (184807, 2016-02-13)
src/ex1/ex1-dataset.js.svg (165117, 2016-02-13)
src/ex1/ex1-linear-regression-multi.js.svg (173346, 2016-02-13)
src/ex1/ex1-linear-regression.js.svg (166450, 2016-02-13)
src/ex1/ex1.jl (3857, 2016-02-13)
src/ex1/ex1_multi.jl (4691, 2016-02-13)
src/ex1/ex1data1.txt (1359, 2016-02-13)
src/ex1/ex1data2.txt (657, 2016-02-13)
src/ex1/featureNormalize.jl (1221, 2016-02-13)
src/ex1/gradientDescent.jl (906, 2016-02-13)
src/ex1/gradientDescentMulti.jl (974, 2016-02-13)
src/ex1/normalEqn.jl (672, 2016-02-13)
src/ex1/plotData.jl (337, 2016-02-13)
src/ex1/submit.jl (1633, 2016-02-13)
src/ex1/warmUpExercise.jl (339, 2016-02-13)
src/ex2/ (0, 2016-02-13)
src/ex2/LogisticRegression.jl (323, 2016-02-13)
src/ex2/costFunction.jl (951, 2016-02-13)
src/ex2/costFunctionReg.jl (970, 2016-02-13)
src/ex2/ex2-dataset-reg.js.svg (174392, 2016-02-13)
src/ex2/ex2-dataset.js.svg (129774, 2016-02-13)
src/ex2/ex2-logistic-regression-reg.js.svg (180677, 2016-02-13)
src/ex2/ex2-logistic-regression.js.svg (129985, 2016-02-13)
src/ex2/ex2.jl (3877, 2016-02-13)
src/ex2/ex2_reg.jl (3102, 2016-02-13)
src/ex2/ex2data1.txt (3775, 2016-02-13)
src/ex2/ex2data2.txt (2233, 2016-02-13)
src/ex2/mapFeature.jl (520, 2016-02-13)
src/ex2/plotData.jl (746, 2016-02-13)
... ...
# Coursera Machine Learning in Julia
## Description
Scripts for [Coursera Stanford Machine Learning](https://www.coursera.org/learn/machine-learning/home/welcome) assignments in Julia.
As to exercises, this repository has only mock methods, so you should implement those first, and then submit the solutions.
## Requirements
Julia v0.4.x
scipy (to read .mat files)
You should also install some Julia libraries, as written in [REQUIRE](https://github.com/homuler/coursera-ml-julia/blob/master/REQUIRE).
## Usage
```shell
cd coursera-ml-julia/src/[exercise]
julia
julia> include("submit.jl") # when submitting
julia> submit()
julia> include("ex1.jl") # when running exercise scripts
```
## ToDo
- Fix comments for Julia
- Migrate from PyPlot to Gadfly or Plotly
- Exercise7
- Plot original data in 3D
- 2D visualization produced using PCA (has some bugs...)
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