Lesson plan

LeNet

LeNet is a series of convolutional neural network architectures created by a research group at AT&T Bell Laboratories between of the period of 1988 to 1998, centered around Yann LeCun. They were designed for reading small grayscale images of handwritten digits and letters, and were used in ATMs for reading cheques. Convolutional neural networks are a kind of feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing. LeNet-5 was one of the earliest convolutional neural networks and was historically important during the development of deep learning. In general, when LeNet is referred to without a number, it refers to the 1998 version, LeNet-5, which is the most well-known version.

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Article summary

LeNet is a series of convolutional neural network architectures created by a research group at AT&T Bell Laboratories between of the period of 1988 to 1998, centered around Yann LeCun. They were designed for reading small grayscale images of handwritten digits and letters, and were used in ATMs for reading cheques. Convolutional neural networks are a kind of feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing. LeNet-5 was one of the earliest convolutional neural networks and was historically important during the development of deep learning. In general, when LeNet is referred to without a number, it refers to the 1998 version, LeNet-5, which is the most well-known version.

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Discussion questions

  1. Summarise the article in your own words. Which single claim would you verify first, and why?
  2. Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
  3. If two sources disagreed about one of these facts, what would you need in order to trust one over the other?

Bibliography

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Key claims and bibliography are generated from the same tracked claims and sources as the article page.