To use tips

These are the few things which are important in ML. I too sometimes forget these.. :P

• Use version control.
• Separate code from data.
• Separate input data, working data and output data.
• Modify input data with care.
• Save everything to disk frequently.
• Separate options from parameters.
• Do not use global variables.
• Record the options used to generate each run of the algorithm.

% store the results
serialise(options, 'options.dat', options.working_path);
serialise(parameters, 'parameters.dat', options.working_path); 

OR

serialise(parameters, 'parameters.dat'], ...
              [options.working_path '_' options_configuration.name]);

• Make it easy to sweep options.

• Make it easy to execute only portions of the code.

run_experiment('dataset_1_options', '|preprocess_data|initialise_model|train_model|'); 

• Use checkpointing.

% set the options
options = ...

% load the data
data = ...

if saved_state_exists(options)

    % load from disk
    [parameters, state] = deserialize_latest_params_state(options.working_path);

    % command line output
    disp(['Starting from iteration ' state.iteration]);

else

    % initialize
    parameters = init_parameters();
    state = init_state();

end

% learn the parameters
parameters = train_model(options, data, parameters, state);

• Write demos and tests.

From  hunch.net which further has its root to link