Training Models - Sage Wisdom

The following is solid advice from Andrej Karpathy on training neural networks and some specific tips to deal with it. Read his post here: A Recipe for Training Neural Networks (by @karpathy)

1. Understand the data. Become one with it. Be the ball.
2. Set up end to end pipeline, with some tips
   • fix and random seed
   • simplify
   • add sig digits to eval
   • verify loss at init
   • init "well"
   • human baseline
   • input-indepent baseline - does random or no data perform worse as expected
   • overfit one batch
   • verify decreasing training loss - push buttons you would expect to break things and see if they do. tap on the gauges when they look right, to see if it's real.
   • use backprop to chart dependencies - find bugs in arch by doing basic tests
   • generalize a special case - don't bite off more than you can chew. start special case, then generalize, reasonably.
3. Overfit
4. Regularize
   • moar data
   • creative data
   • dropout
   • weight decay
   • early stopping
   • decrease batch size
   • moar weights
5. Tune your hyperparameters
6. Squeeze out the juice w/ ensembles and lengthened training

Go read that post. And this one, and the rest of his site, probably.

Yes, You Should Understand Backprop (by @karpathy)