A neuron is a cell that has several inputs that can be activated by some outside process. Neural network terminology is inspired by the biological operations of specialized cells called neurons. We will cover three applications: linear regression, two-class classification using the perceptron algorithm and multi-class classification. This post covers the basics of ANNs, namely single-layer networks. Whether or not this is true (or even provides an advantage in terms of development time) remains to be seen, but currently it's important that we machine learning researchers and enthusiasts have a familiarity with the basic concepts of neural networks. It is theorized that because of their biological inspiration, ANN-based learners will be able to emulate how a human learns to recognize concepts or objects without the time-consuming feature engineering step. One of the more famous examples of Deep Learning is the " Youtube Cat" paper by Andrew Ng et al. However, they have experienced a resurgence with the recent interest and hype surrounding Deep Learning.
Their used waned because of the limited computational power available at the time, and some theoretical issues that weren't solved for several decades (which I will detail at the end of this post).
Artificial neural networks (ANNs) were originally devised in the mid-20th century as a computational model of the human brain.
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