Structuring Machine Learning Projects (Part 1)

Machine learning projects like everything in life needs a concrete plan. Just by feeding machine learning algorithms in the data won’t give you the optimal results. So here’s the couple of blueprints in the bag that you can utilise.

Credit : www.morioh.com

Why strategise Machine Learning?

Let’s start with example. Let’s say you are working on cat classifier and after giving some time training the data, your accuracy came out to be 90%. It is not good enough. Normally, what we do after it is as follows:

• Collect more data
• Collect more diverse training set
• Train algorithm longer with gradient descent
• Try Adam instead of gradient descent
• Try bigger networks
• Try Dropout
• Add L2 regularisation
• Change the Network architecture
• Change the activation function

When trying to improve deep learning learning system we often have a lot of ideas that we can try. The problem is that, if we chose poorly, it is possible that we spend 6 months on wrong direction, only to realise after 6 months that it wasn’t the right way.

Setting up the Goal