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Writer's pictureDR.GEEK

Increasing Accuracy, Complexity and Real-World Impact

(20th-April-2021)


  • Since the 1980s, deep learning has consistently improved in its ability to provide accurate recognition or prediction. Moreover, deep learning has consistently been applied with success to broader and broader sets of applications.

  • The earliest deep models were used to recognize individual objects in tightly cropped, extremely small images (Rumelhart et al., 1986a). Since then there has been a gradual increase in the size of images neural networks could process. Modern object recognition networks process rich high-resolution photographs and do not have a requirement that the photo be cropped near the object to be recognized (Krizhevsky et al.,

Initially, the number of connections between neurons in artificial neural networks was limited by hardware capabilities.


1. Adaptive linear element (Widrow and Hoff, 1960)

2. Neocognitron (Fukushima, 1980)

3. GPU-accelerated convolutional network (Chellapilla et al., 2006)

4. Deep Boltzmann machine (Salakhutdinov and Hinton, 2009a)

5. Unsupervised convolutional network (Jarrett et al., 2009)

6. GPU-accelerated multilayer perceptron (Ciresan et al., 2010)

7. Distributed autoencoder (Le et al., 2012)

8. Multi-GPU convolutional network (Krizhevsky et al., 2012)

9. COTS HPC unsupervised convolutional network (Coates et al., 2013)

10. GoogLeNet (Szegedy et al., 2014a)

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