Difference between revisions of "History of Computers - Deep Learning"
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While Neural Networks send data on a linear route through the hidden layer to the output layer, Deep Learning networks use multiple hidden layers to model high-level abstractions.<ref>http://neuralnetworksanddeeplearning.com/chap6.html</ref> Each layer uses the output from the previous layer as its input. Deep Learning networks. These networks can learn in one of two ways, either supervised – such as image recognition where inputs need to be classified – or unsupervised – where the computer is searching for a pattern. <ref>https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d</ref> | While Neural Networks send data on a linear route through the hidden layer to the output layer, Deep Learning networks use multiple hidden layers to model high-level abstractions.<ref>http://neuralnetworksanddeeplearning.com/chap6.html</ref> Each layer uses the output from the previous layer as its input. Deep Learning networks. These networks can learn in one of two ways, either supervised – such as image recognition where inputs need to be classified – or unsupervised – where the computer is searching for a pattern. <ref>https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d</ref> | ||
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Latest revision as of 23:10, 17 September 2018
Deep Learning
Introduction
Deep Learning was invented by Geoffrey Hinton and is the next generation of Neural Networks.[1] “There are several types of Deep learning,deep neural networks, belief networks and recurrent networks.” [2]
Overview
While Neural Networks send data on a linear route through the hidden layer to the output layer, Deep Learning networks use multiple hidden layers to model high-level abstractions.[3] Each layer uses the output from the previous layer as its input. Deep Learning networks. These networks can learn in one of two ways, either supervised – such as image recognition where inputs need to be classified – or unsupervised – where the computer is searching for a pattern. [4]
Significance
Some of the notable uses of Deep Learning algorithms are drug synthesis, facial recognition, natural language processing and computer vision. [5]
Reference
- ↑ https://ai.google/research/people/GeoffreyHinton
- ↑ https://www.educba.com/neural-networks-vs-deep-learning/
- ↑ http://neuralnetworksanddeeplearning.com/chap6.html
- ↑ https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d
- ↑ https://www.deeplearningbook.org/contents/applications.html
Written by August Windham
