Introduction to Neural Networks

Source: Deep Learning on Medium

IV. Neural Networks Types: RNN and CNN

Recurrent Neural Networks—can be thought of as multiple copies of the same network — where each one passes along a message to the next. RNN introduces the idea of loops in the network, and is very good with processing sequential data like language.

Convoluted Neural Networks — is designed mainly for image data classification —where there are groups of nuerons that share weights learn to identify features. CNN take the input image and processes it then classifies it under a certain category. In CNN the neuron work as a group, with each one examining a different location in the image so as a group the entire image is covered.

V. Deep Learning Past and Present:

In the past there were three main issues holding back Deep Learning.

(1) Back-propagation- error was difficult to attribute because the error would get shared out so through many layers that the error estimates weren’t useful by the time it reached the input layers.

(2) Deep Learning requires a great deal of computing power and works best with a great deal of training data.

(3) Deep Learning requires a great deal of computing power.

With these issues now out of the way — Deep Learning — has been able to thrive and perform deep and accurate analysis.