The key is that deep neural nets are able to break complex patterns down into a series of simpler patterns. For example: let’s say a task is to determine whether or not an image CONTAINED a human face. A deep neural net would first use edges to detect different parts of the face – the nose, lips, ears, eyes etc. and would then combine the results together to form the whole face. This IMPORTANT feature using simpler patterns as building blocks to detect “complex patterns” is what gives deep neural nets their strength.

There is one key downside to all this – deep neural nets take much longer to train. However with the advancement in technology, now there are high performance GPUs available that can finish training a complex net in a relatively QUICKER time compared to those using CPUs.

There are different categories to be able to handle both scenarios where labelled data exist and where there is no labelled data. Different TECHNIQUES / approaches can be used to hand such problems.

Below is correct mapping for the tabular data of Side A to Side B:

*RNTN – Recursive Neural Tensor Network, *MLP – Multi Layer Perceptron, *RELU – Rectifier Linear Unit