With the advent of big data processing infrastructure, GPU, and GP-GPU, we are now able to overcome the challenges with shallow neural networks, namely overfitting and vanishing gradient, using various activation functions and L1/L2 regularization techniques. Deep learning can work on large amounts of labeled and unlabeled data easily and efficiently.
As mentioned, deep learning is a class of machine learning wherein learning happens on multiple levels of neuron networks. The standard diagram depicting a DNN is shown in the following figure:
From the analysis of the previous figure, we can notice a remarkable analogy with ...
