Andrew Ng machine learning

Gradient Descent vs Normal Equation
Normal equation good for smaller feature size

Normal Equation Noninvertibility
too many features (m <= n)
redundant features (linearly dependent)
pinv vs inv in Octave.
Normal equation - regularization makes X'X invertible even if it's not.

Andrew ng clustering & PCA

randomly assign clusters
assign clusters to each instance
re compute clusters
example image compression - choose R,G,B as numerical features and
assign clusters to each point

PCA
----------
example - data compression - reduce n-dimensions to K-dimensions
co variance matrix to capture non axis aligned features' variance(spread)
reconstruct original data by same matrix - U
eigen vectors
example - image compression - choose each pixel as feature - select K
most important ones

scatter3 in octave for 3D-visualization

andrew ng collaborative filtering

recommender systems - collaborative filtering

If you know weights for movie attributes, romance, action etc, you can
learn weights for user preferences.

If you know weights for user preferences, you can compute movie attributes.

If you don't know both, start with a guess for one and compute other.
then reverse. then reverse. until it converges.

But there is another efficient approach which can solve for both together.

mysql : database size query

SELECT table_schema "DB Name", Round(Sum(data_length + index_length) /
1024 / 1024, 1) "DB Size in MB" FROM information_schema.tables GROUP
BY table_schema;

http://stackoverflow.com/questions/1733507/how-to-get-size-of-mysql-database

table size query

SELECT TABLE_NAME, table_rows, data_length, index_length, round(((data_length + index_length) / 1024 / 1024),2) "Size in MB" FROM information_schema.TABLES WHERE table_schema = "schema_name"

Octave indexing

>> tk
tk =

1 2 3
4 5 6
7 8 9

>> tk(1,:) //first row, all columns
ans =

1 2 3

>> tk(:,1) //first column, all rows
ans =

1
4
7

>> tk(1,1) //first row, first column
ans = 1
>> tk(1,2:3) //first row, second/third columns
ans =

2 3

>> tk(2:3,1) //2nd,3rd row, 1st column
ans =

4
7

>> tk(2:3) //If you omit column part, it's 1 by default. Also the result is in row format as opposed to the previous one
ans =

4 7
//flattening in row/column format
>> tk(:)
ans =

1
4
7
2
5
8
3
6
9

>> tk(:)'
ans =

1 4 7 2 5 8 3 6 9