Update Readme

README.md

@@ -8,10 +8,10 @@ Description
 The design goal of the code are as follows:
 
 1. Clean: Code is very succinct. There are little nasty guarding code that distracts readers' attention. As a result, the core of the algorithms can be easily spot.
-2. Efficient: My tricks for making Matlab scripts efficient were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementation. Usually, functions in this package are orders faster than Matlab builtin functions which provide the same functionality (eg. kmeans). If anyone found any Matlab implementation that is faster than mine, I am happy to further optimize.
+2. Efficient: Many tricks for making Matlab scripts efficient were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementation. Usually, functions in this package are orders faster than Matlab builtin functions which provide the same functionality (eg. kmeans). If anyone found any Matlab implementation that is faster than mine, I am happy to further optimize.
 3. Robust: Many numerical stability techniques are applied, such as probability computation in log scale to avoid numerical underflow and overflow, square root form update of symmetric matrix, etc.
 4. Easy to learn: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines.
-5. Practical: The package is designed not only to learn the algorithms in the book, but also to facilitate ML research. Many functions in this package are already among the top downloads in Matlab [file exchange](http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A49739) site and widely used.
+5. Practical: The package is designed not only to be easily read, but also to be easily used to facilitate ML research. Many functions in this package are already among widely used  [file exchange](http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A49739).
 
 
 Installation