Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
Deep Learning is Hierarchical Feature Learning
In addition to scalability, another often cited benefit of deep learning models is their ability to perform automatic feature extraction from raw data, also called feature learning.
Why ‘Deep Learning’ is called deep?
It is because of the structure of ANNs. Earlier 40 years back, neural networks were only 2 layers deep as it was not computationally feasible to build larger networks. Now it is common to have neural networks with 10+ layers and even 100+ layer ANNs are being tried upon.
You can essentially stack layers of neurons on top of each other. The lowest layer takes the raw data like images, text, sound, etc. and then each neurons stores some information about the data they encounter. Each neuron in the layer sends information up to the next layers of neurons which learn a more abstract version of the data below it. So the higher you go up, the more abstract features you learn. You can see in the picture below has 5 layers in which 3 are hidden layers.
In deep learning, ANNs are automatically extracting features instead of manual extraction in feature engineering. Take an example of an image as input. Instead of us taking an image and hand compute features like distribution of colors, image histograms, distinct color count, etc., we just have to feed the raw images in ANN. ANNs have already proved their worth in handling images, but now they are being applied to all kinds of other datasets like raw text, numbers etc. This helps the data scientist to concentrate more on building deep learning algorithms.
What is the Most common thing required for deep learning?
DATA, duh?
Soon, feature engineering may turn obsolete but deep learning algorithms will require massive data for feeding into our models. Fortunately, we now have big data sources not available two decades back — facebook, twitter, Wikipedia, project Gutenberg etc.
