Neural Networks with Python - [Part 2]
In the second video tutorial in this series I discuss some basic concepts of artificial neural networks.
The foundation for this technology dates back to the 1940s; some voices claim that it dates even further back. However, it took a relatively long time for the field to experience solid advances, one of the key reasons being the computational power required to run these algorithms efficiently.
In lay terms an artificial neural network is comprised of artificial neurons (which are the building blocks) which can be connected to each other in different ways. The simplest form of ANN is the perceptron, which we'll talk about in an upcoming tutorial in more details.
The main goal or aspiration of an ANN is to emulate the biological brain. While they have not been used for very complex tasks historically, ANNs are currently the hype in the fields of machine learning; and to be more exact in the subfield of deep learning (which probably became a field in of itself).
Some of the current uses of ANNs are for: computer vision, speech recognition, robotics, medical imagining and diagnosis, traffic analysis, time series data analysis, and much much more. Please see the video below for a little more elaborate explanation.
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Cristi Vlad Self-Experimenter and Author
Well stated. Artificial Intelligence is really an awesome innovation that has hit the global technology.
Although, a negative aspect of this great technology is in the fact that it has led to a loss of job and replacement of humans from some offices that utilizes this technology particularly replacing them with robots.